TWI683997B - System and method for dynamic care area generation on an inspection tool - Google Patents

Abstract

A defect inspection system includes an inspection sub-system and a controller communicatively coupled to the detector. The inspection sub-system includes an illumination source configured to generate a beam of illumination, a set of illumination optics to direct the beam of illumination to a sample, and a detector configured to collect illumination emanating from the sample. The controller includes a memory device and one or more processors configured to execute program instructions. The controller is configured to determine one or more target patterns corresponding to one or more features on the sample, define one or more care areas on the sample based on the one or more target patterns and design data of the sample stored within the memory device of the controller, and identify one or more defects within the one or more care areas of the sample based on the illumination collected by the detector.

Description

System and method for generating dynamic care area on inspection tool priority

This application claims that the title of the application on May 28, 2015, titled NOVEL AND EFFICIENT APPROACH FOR ON-TOOL DYNAMIC CARE AREA GENERATION USING DESIGN, and the provisional Indian patent application with Vijayakumar Ramachandran, Vidyasagar Anantha, Philip Measor, and Rajesh Manepalli as inventors No. 2681/CHE/2015; and the application titled July 30, 2015 is NOVEL AND EFFICIENT APPROACH FOR ON-TOOL DYNAMIC CARE AREA GENERATION USING DESIGN, with Vijayakumar Ramachandran, Vidyasagar Anantha, Philip Measor and Rajesh Manepalli as inventors The priority of US Provisional Patent Application No. 61/198,911, the full texts of these two applications are incorporated herein by reference.

The invention relates generally to defect detection, and more specifically, the invention relates to the creation of a care area on an inspection tool.

The inspection system identifies defects on the semiconductor wafer and classifies the defects to generate a defect group on a wafer. A given semiconductor wafer may include hundreds of chips, each chip containing thousands of components of interest, and each component of interest may have millions of instances for a given layer of a wafer. Therefore, the inspection system can produce a large number of wafers on a given wafer Data points (for example, hundreds of billions of data points used in some systems). In addition, the demand for ever-shrinking devices has led to an increase in demand for inspection systems. Requirements include the need for increased resolution and capacity to infer the root cause of identified defects without sacrificing inspection speed or accuracy.

However, the use of design data associated with wafers often affects the additional consumption of an inspection process and therefore the throughput. For example, a utility program for generating a care area based on design data can provide a large data file specifying various attributes of the care area (which must be sent to the inspection tool). In addition, a testing tool may need to register the design data with the sample to correlate the design coordinates with the testing tool coordinates.

Therefore, it would be desirable to provide a system and method for addressing shortcomings such as those identified above.

According to one or more illustrative embodiments of the invention, a defect detection system is disclosed. In an illustrative embodiment, the system includes a detection subsystem. In another illustrative embodiment, the detection subsystem includes an illumination source configured to generate an illumination beam. In another illustrative embodiment, the detection subsystem includes a set of illumination optics to direct the illumination beam to a sample. In another illustrative embodiment, the detection subsystem includes a detector configured to collect the illumination emitted from the sample. In another illustrative embodiment, the system includes a controller communicatively coupled to the detector. In another illustrative embodiment, the controller includes a memory device and one or more processors configured to execute program instructions. In another embodiment, the controller is configured to determine one or more target patterns corresponding to one or more features on the sample. In another embodiment, the controller is configured to define one or more care areas on the sample based on the one or more target patterns and the design data of the sample. In another illustrative embodiment, the design data of the sample is stored in the memory device of the controller. In another embodiment, the controller is configured to identify the one or more care areas of the sample based on the illumination collected by the detector One or more defects.

According to one or more illustrative embodiments of the invention, a defect detection system is disclosed. In an illustrative embodiment, the system includes a detection subsystem. In another illustrative embodiment, the detection subsystem includes an illumination source configured to generate an illumination beam. In another illustrative embodiment, the detection subsystem includes a set of illumination optics to direct the illumination beam to a sample. In another illustrative embodiment, the detection subsystem includes a detector configured to collect the illumination emitted from the sample. In another illustrative embodiment, the system includes a controller communicatively coupled to the detector. In another illustrative embodiment, the controller includes a memory device and one or more processors configured to execute program instructions. In another embodiment, the controller is configured to determine one or more target patterns corresponding to one or more features on the sample. In another embodiment, the controller is configured to determine a source pattern. In another illustrative embodiment, the source pattern is close to a subset of instances of the one or more target patterns in the design data of the sample. In another illustrative embodiment, the design data of the sample is stored in the memory device of the controller. In another illustrative embodiment, the controller is configured to define between the source pattern and at least one target pattern of the instance subset of the one or more target patterns in the design data of the sample A spatial relationship. In another illustrative embodiment, the controller is configured to identify one or more instances of the source pattern within the design data of the sample. In another illustrative embodiment, the controller is configured to be based on the one or more identified instances of the source pattern and the subset of the instances of the source pattern and the one or more target patterns The spatial relationship between at least one target pattern identifies the instance subset of the one or more target patterns in the design data of the sample. In another illustrative embodiment, the controller is configured to define one or more care areas on the sample based on the instance subset of the one or more target patterns. In another illustrative embodiment, the controller is configured to identify one or more defects in the one or more care areas of the sample based on the illumination collected by the detector.

According to one or more illustrative embodiments of the invention, a defect detection method is disclosed. In an illustrative embodiment, the method includes providing a copy of the design data to a testing system. In another illustrative embodiment, the method includes determining one or more target patterns. In another illustrative embodiment, the one or more target patterns include design data associated with one or more sample features to be detected. In another illustrative embodiment, the method includes defining one or more care areas on the sample by the detection system based on the one or more target patterns and the design data of the sample. In another illustrative embodiment, the method includes identifying one or more defects in the one or more care areas of the sample.

It should be understood that both the foregoing general description and the following detailed description are merely exemplary and illustrative and do not necessarily limit the invention. The accompanying drawings (which are incorporated in and constitute a part of the specification) illustrate embodiments of the invention and together with the general description serve to explain the principles of the invention.

100‧‧‧ detection system

102‧‧‧ Detection subsystem

104‧‧‧Controller

106‧‧‧ processor

108‧‧‧Memory media/memory/memory device

110‧‧‧ sample

202‧‧‧Test tool

204‧‧‧Design Module

206‧‧‧Step

208‧‧‧Analysis of design data

210‧‧‧Recipe Module

212‧‧‧Step

214‧‧‧Step

216‧‧‧Step

300‧‧‧Method

302‧‧‧Step

304‧‧‧Step

306‧‧‧Step

308‧‧‧Step

402‧‧‧Design materials

404‧‧‧ target pattern

406‧‧‧care area

408‧‧‧ source pattern

410‧‧‧Combined target pattern

412‧‧‧ Specific example of target pattern

414‧‧‧ vector

502‧‧‧illumination source/particle source

504‧‧‧Illumination beam/particle beam

506‧‧‧Illumination path

508‧‧‧Optical components

510‧‧‧Lens

512‧‧‧Sample stage

514‧‧‧Beam splitter

516‧‧‧Objective

518‧‧‧ Collection path

520‧‧‧Lens

522‧‧‧detector

524‧‧‧Particle focusing element

526‧‧‧Particle scanning element

528‧‧‧particle

Those skilled in the art can better understand the many advantages of the present invention by referring to the drawings, in which: FIG. 1 is a conceptual diagram illustrating a detection system according to one or more embodiments of the present invention; FIG. 2 is based on A block diagram of an inspection tool of an inspection system according to one or more embodiments of the present invention, which illustrates the use of design data to generate a care area for inspection based on the design data stored on the inspection tool; FIG. 3 is an illustration A flowchart illustrating one of the steps performed in a method for defect detection according to one or more embodiments of the present invention; FIG. 4 is a diagram illustrating the definition of association based on a source pattern according to one or more embodiments of the present invention A schematic diagram of the design data of the care area; FIG. 5A is a conceptual diagram illustrating an optical detection subsystem according to one or more embodiments of the present invention; and FIG. 5B is a utilization diagram according to one or more embodiments of the present invention. Or of multiple particle beams A simplified schematic of one of the detection subsystems.

Reference will now be made in detail to the disclosed subject matter, which is illustrated in the accompanying drawings.

The embodiment of the invention relates to a detection system for generating a same care area on a tool. In this regard, the care area for detection or the selected area of the sample of interest can be directly generated on the detection tool. An additional embodiment of the invention relates to identifying a care area on a tool based on identifying one or more instances of a target pattern of interest in the design data of a sample stored on the inspection tool. For example, a target pattern may include design data associated with one or more sample features to be detected. An additional embodiment of the present invention relates to storing and preprocessing sample design data on an inspection system for effective determination of the care area on the inspection tool. A further embodiment of the present invention relates to identifying a subset of examples of target patterns in a design pattern based on proximity to a source pattern in the design pattern of the sample. Therefore, the generation of the care area may include searching for design data of a combination of target patterns of interest based on a defined spatial relationship for one of the instances of the target pattern filtered to include the target pattern close to the source pattern.

It should be recognized in this article that inspection tools can usually detect only a subset of the surface of a sample for defects. The generation of the care area or target area of the sample to be detected can not only significantly improve the efficiency of defect detection by reducing the detected surface area, but also significantly improve the accuracy of defect detection by reducing spurious signals and noise. In addition, the care area may be defined to provide target detection analysis, such as, but not limited to, analysis of a specific defect type or analysis of a specific pattern element positioned throughout the sample.

It should be further recognized that the design data of the sample (for example, the physical layout of the components on the same sample, the electrical connection between the components on the sample, or the like) can be used to define the care area. However, the use of design data usually affects the additional consumption and therefore the processing volume of a testing procedure. For example, a utility for generating care areas based on design data can provide designation Large data files of various attributes of the care area (for example, the location of each care area on the same copy, the shape of each care area, and the like) must be transmitted to the inspection tool. In addition, an inspection tool may need to register the design data with the sample (e.g., alignment, scaling, or the like) to match the design coordinates (e.g., graphic design system (GDS) coordinates or the like) with the coordinates of the inspection tool Interrelated.

The embodiment of the invention relates to storing a pre-processed version of the design data of a sample on a testing tool. In this regard, pre-processing design data (eg, a local version of the pre-processing design data) can be used on the inspection tool to generate a design-based care area. In addition, in some embodiments, a design-based care area can be created on the inspection tool without further data transfer requirements. In addition, the design-based care area generated on the inspection tool can be automatically aligned to the coordinates of the inspection tool.

As used throughout the present invention, the term "sample" generally refers to a substrate formed from a semiconductor or non-semiconductor material (eg, a wafer or the like). For example, a semiconductor or non-semiconductor material may include (but is not limited to) single crystal silicon, gallium arsenide, and indium phosphide. A sample may contain one or more layers. For example, such layers may include (but are not limited to) a photoresist, a dielectric material, a conductive material, and a semi-conductive material. Many different types of such layers are known in the art, and the term sample as used herein is intended to include a sample on which all types of such layers can be formed. One or more layers formed on a sample may be patterned or unpatterned. For example, a sample may include a plurality of crystal grains, each of which has re-patternable features. The formation and processing of these material layers can ultimately lead to a completed device. Many different types of devices can be formed on a sample, and the term sample as used herein is intended to include a sample on which any type of device known in the art is being manufactured. Furthermore, for the purposes of the present invention, the terms sample and wafer should be interpreted as interchangeable. In addition, for the purposes of the present invention, the terms patterned device, mask and reticle have been interpreted as interchangeable.

FIG. 1 illustrates a conceptual diagram of a detection system 100 according to one or more embodiments of the invention. In one embodiment, the detection system 100 includes a detection subsystem 102 to Detect defects on sample 110.

It should be noted herein that the inspection subsystem 102 may be any type of inspection system known in the art that is suitable for detecting defects on the sample 110. For example, the detection subsystem 102 may include a particle beam detection subsystem. Therefore, the detection subsystem 102 can direct one or more particle beams (eg, electron beams, ion beams, or the like) to the sample 110 so that it can be based on detected radiation (eg, secondary electrons) emitted from the sample 110 , Backscattered electrons, luminescence or the like) to detect one or more defects. As another example, the detection subsystem 102 may include an optical detection subsystem. Therefore, the detection subsystem 102 can direct optical radiation to the sample 110 so that a detected radiation (e.g., reflected radiation, scattered radiation, diffracted radiation, luminous radiation, or the like) emitted from the sample 110 can be detected. Or multiple defects.

The detection subsystem 102 can operate in an imaging mode or a non-imaging mode. For example, in an imaging mode, individual objects (eg, defects) may be resolved within the illuminated point on the sample (eg, as part of a bright field image, a dark field image, a phase contrast image, or the like). In a non-imaging mode of operation, the radiation collected by one or more detectors may be associated with a single illumination point on the sample and may represent a single pixel of an image of the sample 110. In this regard, an image of the sample 110 can be generated by obtaining data from the sample location array. In addition, the detection subsystem 102 is operable as a scattering measurement-based detection system in which the radiation from the sample is analyzed at a pupil plane to characterize the angular distribution of the radiation from the sample 110 (e.g. (The radiation is scattered and/or diffracted).

In another embodiment, the detection system 100 includes a controller 104 coupled to the detection subsystem 102. For example, the controller 104 can be communicatively coupled to the detector 522. In this regard, the controller 104 may be configured to receive data, including (but not limited to) detection data from the detection subsystem 102. In another embodiment, the controller 104 includes one or more processors 106. For example, the one or more processors 106 may be configured to execute a set of program instructions maintained in a memory device 108 or memory. One or more processors of a controller 104 106 may include any processing element known in the art. In this sense, one or more processors 106 may include any microprocessor-type device configured to execute algorithms and/or instructions. In addition, the memory medium 108 may include any storage medium known in the art that is suitable for storing program instructions executable by the associated processor or processors 106. For example, the memory medium 108 may include a non-transitory memory medium. As an additional example, the memory medium 108 may include (but is not limited to) a read-only memory, a random access memory, a magnetic or optical memory device (eg, a magnetic disk), a magnetic tape, a solid-state magnetic Disc players and the like. It should be further noted that the memory medium 108 may be housed in a common controller housing together with one or more processors 106.

The inspection system 100 can utilize any inspection technique known in the art to detect defects associated with a sample. For example, by comparing the measured characteristics of the sample (eg, generated by the detection subsystem 102 or the like) with the measured characteristics of a reference sample (eg, die-to-die (D2D) detection, standard reference crystal Particles (SRD) or similar) to detect defects on the sample 110. As another example, a defect on the sample 110 may be detected by comparing one of the inspection images of the sample 110 with an image based on design characteristics (eg, die-to-database (D2DB) inspection). As a further example, the detection system 100 may include a virtual detection system. In one embodiment, the controller 104 operates as a virtual detector. In this regard, the controller 104 can detect one or more defects on the sample 110 by comparing the detected data of the sample with the continuous reference data (eg, one or more reference images). For example, one or more reference images may be stored on the inspection system 100 (eg, in the memory 108) and used for defect detection. In another embodiment, the controller 104 generates and/or receives a simulated inspection image based on the design data associated with the sample 110 to operate as a reference image for defect detection.

A detection system using design data is generally described in US Patent Application No. 2014/0153814 published on June 5, 2013, and the entire text of the case is incorporated herein by reference. U.S. Patent No. 8,126,255 issued on February 28, 2012 generally describes the use For the detection system of continuous data (for example, stored data), the entire text of this patent is incorporated herein by reference. In US Patent No. 7,676,077 issued on March 9, 2010 and US Patent No. 6,154,714 issued on November 28, 2000, the general description of the inspection system using the same design data to facilitate the inspection, the full text of these patents Incorporated by reference. The defects and failure sources are generally described in US Patent No. 6,920,596 issued on July 19, 2005, US Patent No. 8,194,968 issued on June 5, 2015, and US Patent No. 6,995,393 issued on February 7, 2006 It is determined that the entire text of these patents is incorporated herein by reference. US Patent No. 8,611,639 issued on December 17, 2013 generally describes device property extraction and monitoring. The use of dual-energy electron overflow for neutralization in a charging substrate is generally described in US Patent No. 6,930,309 issued on August 16, 2005, the entire content of which is incorporated herein by reference. U.S. Patent No. 6,529,621 issued on March 4, 2003, U.S. Patent No. 6,748,103 issued on June 8, 2004, and U.S. Patent No. 6,966,047 issued on November 15, 2005 generally described the use of photomasks for In the detection system, the entire text of these patents is incorporated herein by reference. U.S. Patent No. 6,691,052 issued on February 10, 2004, U.S. Patent No. 6,921,672 issued on July 26, 2005, and U.S. Patent No. 8,112,241 issued on February 7, 2012 generally describe the generation of a test procedure or To detect the target, the entire text of these patents is incorporated herein by reference. In US Patent No. 6,948,141 issued on September 20, 2005, the key areas for determining semiconductor design data are roughly described, and the entire contents of the patent are incorporated herein by reference.

2 is a block diagram of an inspection tool 202 of an inspection system 100 according to one or more embodiments of the present invention, which illustrates the definition of a care area on the inspection tool 202. In one embodiment, the inspection tool 202 includes one or more modules configured to perform one or more steps of the inspection tool 202. For example, one or more modules of the inspection tool 202 may (but need not) be implemented as stored in the memory 108 and executed by one or more processors 106 to execute one or more program instructions.

In another embodiment, the inspection tool 202 includes a design module 204. For example, the design module 204 may include design data associated with one or more samples 110 to be inspected by the inspection tool 202. In this regard, the design data associated with the sample 110 may be used on the inspection tool 202 to generate a care area. It should be noted in this article that directly generating a design-based care area on the inspection tool 202 can promote the effective and dynamic generation of the care area. For example, creating a design-based care area on the inspection tool 202 can reduce data transfer between the inspection tool 202 and an external system (eg, data transfer for the definition of a care area or the like). In addition, the creation of a design-based care area on the inspection tool 202 may facilitate the precise alignment of the coordinates associated with the design data to the coordinates associated with the sample and/or the inspection tool 202. For example, the design coordinates (eg, GDS coordinates or the like) may need to be adjusted (eg, scaled, rotated, or the like) so that the size and orientation of the design patterns of the design data match the amount on the sample as measured by the inspection tool 202 Test printing patterns. Creating a design-based care area on the inspection tool can facilitate accurate and effective alignment of the design and the sample coordinate system.

The term "design data" as used in the present invention generally refers to the physical design of an integrated circuit and data derived from the physical design through complex simulations or simple geometric and Boolean operations. In addition, an image of a mask and/or its derivatives obtained by a mask inspection system can be used as an agent or agents for design data. This mask image or its derivative can serve as an alternative to the design layout for the design data used in any of the embodiments described herein. In U.S. Patent No. 7,676,007 issued by Kulkarni on March 9, 2010; U.S. Patent Application No. 13/115,957 filed by Kulkarni on May 25, 2011; issued by Kulkarni on October 18, 2011 U.S. Patent No. 8,041,103; and U.S. Patent No. 7,570,796 issued by Zafar et al. on August 4, 2009 describe design information and design information agents, all of which are incorporated herein by reference. In addition, U.S. Patent Application No. 13/339,805 filed on February 17, 2012 generally describes the use of design data in the guided inspection procedure, the entire text of which is incorporated herein by reference.

The design data may include the characteristics of individual components and/or layers on the sample 110 (eg, an insulator, a conductor, a semiconductor, a well, a substrate, or the like), one of the connections between the layers on the sample 110 The capacity relationship or the physical layout of one of the components and connectors (eg, wires) on the sample 110. In this regard, the design data may include a plurality of design pattern elements corresponding to the printed pattern elements on the sample 110.

It should be noted in this article that the design data may include data called a "graphic design", which contains the placement information of the pattern elements on the sample 110. It should be further noted in this article that this information can be extracted from the physical design of a chip usually stored in the GDSII or OASIS file format. Structural behavior or programming interaction can vary according to the content context (environment) of a pattern element. By using planar design, the proposed analysis can identify pattern elements within the design data, such as polygons describing the features to be constructed on a semiconductor layer. In addition, the proposed method can provide coordinate information and content context data (eg, the location of adjacent structures or the like) of these repeated blocks.

In one embodiment, the design data includes one or more graphical representations of pattern elements (eg, visual representations, symbolic representations, graphical representations, or the like). For example, the design data may include a graphical representation of the physical layout of the component (eg, a description corresponding to one or more polygons of the printed pattern elements manufactured on the sample 110). In addition, the design data may include a graphical representation of one or more layers of the original design (eg, one or more layers of printed pattern elements fabricated on the sample 110) or the ability to connect between the one or more layers. As another example, the design data may include a graphical representation of the electrical connection capabilities of the components on the sample 110. In this regard, the design data may include a graphical representation of one or more circuits or sub-circuits associated with the sample. In another embodiment, the design data includes one or more image files containing a graphical representation of one or more portions of the sample 110.

In another embodiment, the design data includes one or more text descriptions of the connection capabilities of the pattern elements of the sample 110 (eg, one or more lists, one or more tables, one or more databases, or the like) . For example, the design data may include (but not limited to) wiring comparison table data Materials, circuit simulation data or hardware description language data. The wiring comparison table may include any type of wiring comparison table known in the art to provide one of the descriptions of the connection capabilities of a circuit, including (but not limited to) a physical wiring comparison table, a logical wiring comparison table, an example-based Wiring comparison table or network-based wiring comparison table. In addition, a wiring reference table may include one or more sub wiring reference tables (for example, in a hierarchical configuration) to describe circuits and/or sub-circuits on the sample 110. For example, the wiring table data associated with a wiring table may include (but is not limited to): a list of nodes (eg, networks, wires, or the like between components of a circuit); a list of ports (eg, Terminal, pin, connector or similar); description of one of the electrical components (eg, resistors, capacitors, inductors, transistors, diodes, power supplies, or the like) between networks; related to electrical components The associated value (for example, a resistance value of a resistor (in ohms), a voltage value of a power supply (in volts), a frequency characteristic of a voltage source, initial conditions of components, or the like). In another embodiment, the design data may include one or more wiring comparison tables associated with specific steps of a semiconductor process flow. For example, the sample 110 may be detected at one or more intermediate points in a semiconductor program flow (eg, by the system 100). Therefore, at a current point in the semiconductor program flow, the design data used to generate the care area can be dedicated to the layout of the sample 110. In this regard, it can be derived from a physical design layout incorporating a technical file (layer connection capabilities, electrical properties of each of the layers and the like) or a wiring comparison table associated with a final layout of a sample 110 (eg, (Extract or the like) A wiring comparison table associated with a specific intermediate point in a semiconductor process flow to include components that exist on the wafer only at a specific intermediate point in the semiconductor process flow.

In another embodiment, the design module 204 of the inspection tool 202 performs a step 206 of preprocessing design data. It should be noted here that the design data may include data not related to the determination of the care area of the inspection system 100 (for example, manufacturing data or the like). In addition, the design data may not be in a format suitable for effective identification (eg, searching, matching, or the like) of the pattern elements of interest in the design data. Therefore, the preprocessed design data may include A version of the accounting data that is pre-processed to facilitate the effective generation of care areas on the inspection tool 202. In this regard, the preprocessed design data can facilitate the identification of one or more instances of target patterns (eg, target elements of interest, hotspots, or the like) within the design data. For example, preprocessed design data can be searched based on any combination of design data elements, including (but not limited to) an identifier of a target pattern, an electrical characteristic of a target pattern, a physical characteristic of a target pattern, or a target pattern A relationship with one or more additional patterns (eg, an anchor pattern, a source pattern, or the like) or a graphical representation of a target pattern.

In another embodiment, the design tool 202 stores design data (eg, original design data, pre-processed design data, or a combination thereof). For example, the design data may be stored in a memory device 108 of the controller 104. In another embodiment, the design data may be pre-processed outside the inspection system 100 and stored on the inspection tool 202. In this regard, the pre-processed design data associated with one or more samples can be sent to the inspection tool 202.

In another embodiment, the design module 204 performs a step of analyzing the design data stored on the inspection tool 202. In this regard, the design module 204 can identify one or more instances of a target pattern within the same design data (eg, by searching for preprocessed design data or similar for one or more target pattern instances By). In addition, the design module 204 may provide parameters of the identified instance of the target pattern required to generate the care area, such as (but not limited to) the coordinates and/or shape of the identified target pattern.

In one embodiment, the inspection tool 202 includes a recipe module 210. For example, the recipe module 210 can use the inspection tool 202 to generate a recipe for one or more inspection steps. In this regard, a recipe may include (but is not limited to) one or more care areas for defect detection, one or more registration operations (e.g., align and/or scale the coordinates associated with the design data To a coordinate or the like associated with the sample and/or inspection subsystem 102), one or more defect identification steps or one or more defect classification steps. In addition, creating a design-based care area on the inspection tool 202 can facilitate an effective multi-step inspection procedure (For example, for system defect discovery or the like). In this regard, it is possible to search design data for different target patterns or combinations of target patterns on the inspection tool 202 according to an iterative inspection analysis without sending the data to an external system.

In another embodiment, the recipe module 210 performs determination to determine one or more target patterns (eg, one or more concerns) associated with the manufacturing pattern elements on the sample 110 to be detected by the detection tool 202 in a detection step Pattern element, one or more hot spots or the like) in step 212. For example, the recipe module 210 may provide one or more target patterns based on one or more purposes of a detection run of one of the detection tools 202. For example, the recipe module 210 may provide a target pattern associated with a known defect type of interest.

In another embodiment, the recipe module 210 determines one or more target patterns in an automated process. For example, the recipe module 210 may analyze the design data 208 of the sample 110 to determine one or more target patterns that may exhibit defects (eg, based on physical layout, pattern size, proximity to other patterns, circuit complexity, or the like Associated characteristics).

In another embodiment, a user facilitates the determination of the target pattern. For example, a user may provide an input to the inspection tool 202 (eg, an input to the recipe module 210), including (but not limited to) one or more defect identifiers, one or more GDS coordinates, one or Multiple design-based classification (DBC) segments or one or more design-based grouping (DBG) cells. In this regard, the recipe module 210 may determine one or more target patterns based on user input. In another embodiment, the inspection tool 202 may provide a visual display associated with the design data (eg, within a design view of the inspection tool 202). In this regard, the user can select one or more target patterns from the visual display of the design data. For example, the visual display may include a graphic display (eg, a display of an image or the like) in which design pattern elements of design data (eg, pattern elements associated with the physical layout of the component, and between the components The pattern element or the like associated with the electrical connector). As another example, the visual display may include a text-based display in which design information may be displayed. In another embodiment, a user can use a landmark system (eg, GDS coordinates) (for example, on a graphic display) to visualize design data to determine and/or confirm one or more target patterns. For example, a user may input coordinates (eg, into an input device of the inspection system 100) to visualize and/or confirm design data at a designated location to generate a target pattern for inspection.

In another embodiment, the recipe module 210 performs a step 214 that defines one or more care areas to be tested on the sample 110. For example, the recipe module 210 may define one or more care areas based on one or more target patterns and design data stored on the inspection tool 202. In one embodiment, the recipe module 210 and the design module 204 interface to analyze the design data based on one or more determined target patterns. In this regard, the recipe module 210 can provide one or more target patterns to the design module 204 for pattern matching. In addition, the design module 204 can identify one or more instances of the target pattern in the design data and provide any parameters required to generate the care area to the recipe module 210 based on the identified instances of the target pattern. For example, the design module 204 may provide the location of the identified instance of the target pattern (eg, in a design coordinate), the shape of the identified instance of the target pattern, the outline of the identified instance of the target pattern, or the like.

In another embodiment, the recipe module 210 performs a step 216 of identifying defects on the sample 110. In this regard, the recipe module 210 can interface with the inspection subsystem 102 to perform defect inspection. In addition, the recipe module 210 can analyze the data received by the inspection subsystem 102 to determine the existence of one or more defects. In addition, the recipe module 210 can characterize one or more defects. For example, the recipe module may (but need not) characterize defects based on a DBC system, a DBG system, or the like. In addition, the recipe module 210 may assign one or more defect identifiers to one or more characterized defects.

It should be recognized herein that the steps described throughout the present invention (eg, the steps associated with the modules of the inspection tool 202 or the like) can be performed by a single controller 104 or alternatively multiple controllers 104. It should be further noted herein that one or more controllers 104 can be positioned close to the detection subsystem 102. In addition, the one or more controllers 104 and detectors The system 102 is housed together in a common housing. In addition, any controller or combination of controllers can be individually packaged as a module suitable for integration into a complete inspection system 100. For example, a first controller may be configured to perform the steps associated with the design module 204. Then, one or more additional controllers may be configured to perform the steps associated with the recipe module 210. In this regard, one or more controllers 104 can be integrated into the detection system 100.

3 is a flowchart illustrating one of the steps performed in a method 300 for defect detection according to one or more embodiments of the present invention. Applicants should note that the embodiments and enabling techniques previously described in the context of the system 100 herein should be interpreted as extending to the method 300. However, it should be further noted that the method 300 is not limited to the architecture of the system 100.

In one embodiment, the method 300 includes a step 302 of providing a copy of the design data to a testing system. For example, design data may (but need not) be provided to a testing system in the form of one or more data files (eg, GDSII files, OASIS files, or the like). In this regard, the design data provided to the inspection system can be used to generate one or more design-based care areas for inspection.

In another embodiment, the method 300 includes a step 304 of determining one or more target patterns. In this regard, one or more target patterns of interest associated with manufacturing features on the sample may be provided for detection. For example, the target pattern may include one or more polygons (for example, one or more polygons representing the features to be constructed on a semiconductor layer (eg, cross, plus, L, T, square, rectangle, or other polygons , With specific dimensions and spacing between items).

In one embodiment, one or more target patterns are determined based on the defect identifier. In this regard, one or more known defects or defect types associated with a defect identifier (eg, an identifier or the like used to classify one or more defects) may be associated with one or more specific target patterns (Eg, based on one or more previously detected strokes, based on one or more design characteristics, or the like). Therefore, the occurrence of known defects or defect types can be characterized by providing corresponding target patterns for detection.

In another embodiment, one or more target patterns are determined based on a previous detection step (eg, by the detection system 100 or an additional detection system). For example, in system defect discovery, a first inspection run on a sample 110 or a portion of the sample 110 may identify one or more manufacturing components of the sample 110 that are susceptible to defects. In this regard, the first inspection pass may determine one or more target patterns associated with the identified manufacturing components on the sample 110. In addition, a second inspection pass may include a recipe (eg, generated by the recipe module 210) to perform a dedicated inspection that identifies one or more target patterns from the first inspection pass. In another embodiment, one or more target patterns are determined according to a DBC or a DBG program associated with a previous detection step.

In another embodiment, one or more target patterns are determined based on one or more coordinates (eg, GDS coordinates or the like) of a target pattern on the sample. For example, one or more target patterns may be determined based on the known coordinates of an exemplary target pattern of interest associated with the design data. As another example, one or more target patterns may be determined based on the known coordinates of an exemplary manufacturing component on the sample 110. Therefore, one or more target patterns associated with the exemplary manufacturing component may be provided for inspection.

In another embodiment, the method 300 includes a step 306 of defining one or more care areas on the sample based on the target pattern and the design data of the sample by the inspection system. In this regard, step 306 may include defining one or more regions on the sample to be detected. For example, a care area may contain coordinates on the sample to be tested (for example, in the coordinate system of the detection system).

In another embodiment, a care area includes one or more target areas on the sample for detection. For example, a first target area may include one or more instances of a first target pattern identified in step 304, and a second target area may include one or more of a second target pattern identified in step 304 Individual cases, and the like. In addition, the definition of one or more target areas may facilitate the sensitive detection of the sample 110. For example, the target area may be defined to include samples with similar sensitivity levels. Therefore, a different sensitivity threshold can be used The value detects each target area so that the contrast of the detection data associated with each target area can be increased.

In another embodiment, step 306 includes identifying one or more instances of the target pattern determined in step 304 within the design data (eg, pre-processed design data stored in the memory device 108 of the inspection system 100). In this regard, each identified instance of the target pattern of interest may be included in a care area. In addition, changes in the target pattern of interest (eg, a target pattern is flipped horizontally and/or vertically, a target pattern is scaled, a target pattern is rotated, or the like) can be identified in step 306 And included in a care area.

Examples of target patterns in device data can be identified using any method known in the art. For example, step 306 may include searching design data for one or more instances of the target pattern to generate one or more identified instances of the target pattern. In one embodiment, step 306 includes a text-based search of one of the design data. For example, text-based design data (eg, one or more lists, one or more tables, one or more databases, one or more data files, or the like can be searched based on one or more characteristics of a target pattern ). In another embodiment, step 306 includes image-based search of one of the design data. For example, an image processing algorithm (such as (but not limited to) a feature extraction technique, a convolution technique, a pattern matching technique, a spatial frequency analysis, a transformation technique (for example, a Hough transformation technique or Similar)) Find one or more instances of a target pattern (or a variation of a target pattern). In addition, multiple design layers of design data (eg, multiple layers corresponding to the manufacturing components on the sample 110) may be individually searched for one or more instances of the target pattern of interest.

In one embodiment, the design data contained in a design layout file (such as OASIS or GDS) can be used to identify the target pattern. It should be noted herein that the size of the target pattern can vary and can be located at various levels of design data (eg, associated with various layers, dies, blocks, cells, or the like of the sample 110). In this regard, a known or The observed design unit hierarchy identifies the target pattern in the design data. For example, a design unit hierarchy can be analyzed to identify target patterns in repeating groups within a given set of inspection data.

In another embodiment, a design rule check (DRC) procedure, an optical rule check (ORC), or a failure analysis (FA) procedure may be used to identify target patterns to identify target patterns that are critical to device performance. In another embodiment, a program window qualification method (PWQ) can be used to identify the target pattern. The search for design data for one or more target patterns can be performed as described by Kulkarni et al. and Zafar et al. in the above reference cases, which are incorporated by reference above.

In some embodiments, data from electronic design automation (EDA) tools and other knowledge can be used to identify target patterns on semiconductor wafers. Any such information about the design generated by an EDA tool can be used to identify duplicate blocks. In addition, the design data can be searched for one or more target patterns in any suitable manner. For example, searching for design data for one or more target patterns can be performed as described in the patent applications referenced above by Kulkarni et al. and Zafar et al., which are incorporated by reference above. In addition, any other method or system described in this patent application can be used to select or identify the target pattern.

In addition, the design data can be analyzed in order to identify appropriate target patterns for inspection based on a given inspection technique (eg, optical inspection, electron beam inspection, and the like).

It should be recognized that the target pattern may be repeated throughout the grains of the sample 110, thereby forming a repeating block (or field). In addition, cells of a sample 110 are sometimes repeated under a different name throughout a given die or may be repeated at multiple locations with one name. In some embodiments, the repeating units are aligned on the same horizontal and/or vertical axis. In other embodiments, the repeating units are not aligned on the same horizontal and/or vertical axis.

In another embodiment, step 306 includes providing a confidence measure associated with the identification of each instance of the target pattern of interest to a location within the design data. In this regard, install An example of a target pattern in the configuration data may include an exact match (for example, a 100% confidence measure or the like) or a substantial match (for example, a less than 100% confidence measure). It should be understood that any credibility measure in this technology is within the spirit and scope of the present invention. For example, a confidence measure may range from 0 (mismatch) to 1 (exact match).

It should be noted herein that the care area may be defined to include a subset of the identified instances of the target pattern. For example, the possibility that a particular defect on a particular component of a device fabricated on a sample 110 will cause a degradation of performance may depend on multiple factors, such as but not limited to the presence of adjacent structures or the operating conditions of a particular component .

In one embodiment, step 306 includes defining an instance of one or more care areas to include a target pattern that is close to an additional pattern in the design data (eg, a source pattern, an anchor pattern, or the like). In this regard, the presence of a source pattern is operable as a filter to provide a subset of instances of the target pattern as the care area to be detected.

FIG. 4 is a schematic diagram illustrating design data for defining an associated care area based on a source pattern according to one or more embodiments of the present invention.

In one embodiment, the design data 402 includes multiple instances of the target pattern 404. In addition, the design data 402 includes a source pattern 408 close to a subset of instances of the target pattern 404 (eg, a specific instance 412 of the target pattern 404). For example, a source pattern may include (but is not limited to) one or more instances of cross, cross, plus, L, T, square, rectangle, or any other polygon, with specific dimensions and spacing.

In addition, step 306 may include defining a care area 406 around the specific instance 412 of the target pattern 404 based on a spatial relationship between a specific instance 412 of the target pattern 404 and the source pattern 408. For example, a spatial relationship between the specific instance 412 of the target pattern 404 and the source pattern 408 may include (but is not limited to) a vector 414 between the specific instance 412 of the target pattern 404 and the source pattern 408. In another embodiment, step 306 includes searching for one or more instances of the source pattern in the design data and further identifying instances of the target pattern based on the spatial relationship between the specific instance 412 of the target pattern 404 and the source pattern 408 A subset of items (e.g. Specific examples 412) of the target pattern 404 are included in a care area. In another embodiment, step 306 includes searching for an instance of a combined target pattern 410 that includes one of the source pattern 408 and one of the target patterns in the design data 402, while surrounding the target pattern associated with the identified combined target pattern 410 A subset of the instances of 404 (eg, specific instances 412 of the target pattern 404) define a care area 406. Therefore, the source pattern 408 can be used as part of a search step and not included in the associated care area 406.

In another embodiment, the method 300 includes a step 308 of identifying one or more defects in one or more care areas of the sample. In this regard, the inspection system (eg, inspection system 100) detects the care area of the sample defined in step 308 for defects (eg, using lighting subsystem 101). For example, data from inspection subsystem 102 may be analyzed to determine the presence of one or more defects on sample 110 associated with the care area defined in step 306. In addition, the identified defects may be classified (eg, based on defect identifiers, DBC fragments, DBG bins, or the like). In another embodiment, data associated with one or more identified defects may be provided (eg, as feed-forward data, feedback data, or the like) to the inspection system 100 and/or an external system.

It should be recognized herein that the steps described throughout the present invention can be performed by a single controller 104 or alternatively multiple controllers 104. It should be further noted herein that one or more controllers 104 may be housed in a common housing or multiple housings. In this way, any controller or combination of controllers can be individually packaged as a module suitable for integration into a complete inspection system 100. By way of a non-limiting example, a first controller can be configured to perform the step of identifying a set of lighting detection events based on a lighting signal received from the lighting sensor. Next, one or more additional controllers can be configured to perform the following steps: identify a set of radiation detection events based on one or more radiation signals received from one or more radiation sensors; compare the set of radiation detections Events and the set of lighting detection events to generate a set of coincident events; and excluding the set of coincident events from the set of lighting detection events to generate a set of identified features on the sample.

FIG. 5A is a conceptual diagram of a detection subsystem 102 configured as an optical detection subsystem according to one or more embodiments of the invention. In one embodiment, the detection subsystem 102 includes an illumination source 502. The illumination source 502 may include any illumination source known in the art that is suitable for generating an illumination beam 504 (eg, a photon beam). For example, the illumination source 502 may include (but is not limited to): a monochromatic light source (eg, a laser); a polychromatic light source having a spectrum including two or more discrete wavelengths; a broadband light source; or One wavelength sweeps the light source. In addition, the illumination source 502 may (but is not limited to) a white light source (for example, a broadband light source having a spectrum including a visible wavelength), a laser source, a free-form illumination source, a monopole illumination source, A multi-pole illumination source, an arc lamp, an electrodeless lamp or a laser sustaining plasma (LSP) source is formed. In addition, the illumination beam 504 can be delivered via free space propagation or guiding light (eg, an optical fiber, a light pipe, or the like).

In another embodiment, the illumination source 502 directs one or more illumination beams 504 to the sample 110 via an illumination path 506. The illumination path 506 may include one or more lenses 510. In addition, the illumination path 506 may include one or more additional optical components 508 suitable for modifying and/or adjusting one or more illumination beams 504. For example, one or more optical components 508 may include, but are not limited to, one or more polarizers, one or more filters, one or more beam splitters, one or more diffusers, one or more Homogenizer, one or more apodizers or one or more beam shapers. In one embodiment, the illumination path 506 includes a beam splitter 514. In another embodiment, the detection subsystem 102 includes an objective lens 516 to focus one or more illumination beams 504 onto the sample 110.

The illumination source 502 may direct one or more illumination beams 504 to the sample at any angle via the illumination path 506. In one embodiment, as shown in FIG. 5A, the illumination source 502 directs one or more illumination beams 504 to the sample 110 at a normal incidence angle. In another embodiment, the illumination source 502 directs one or more illumination beams 504 to the sample 110 at an illegal incidence angle (eg, a glancing angle, a 45 degree angle, or the like).

In another embodiment, the sample 110 is positioned to fix the sample during the scan One of 110 is on the sample stage 512. In another embodiment, the sample stage 512 is an actuatable stage. For example, the sample stage 512 may include, but is not limited to, one or more of the sample 110 adapted to be selectively translated along one or more linear directions (eg, x direction, y direction, and/or z direction) Pan the stage. By another example, the sample stage 512 may include, but is not limited to, one or more rotating stages adapted to selectively rotate the sample 110 along a direction of rotation. By another example, the sample stage 512 may include, but is not limited to, a rotating stage and a rotating stage adapted to selectively translate the sample along a linear direction and/or rotate the sample 110 along a rotational direction Pan the stage.

In another embodiment, the illumination path 506 includes one or more beam scanning optics (not shown) adapted to scan the illumination beam 504 across the sample 110. For example, the one or more illumination paths 506 may include any type of beam scanner known in the art, such as but not limited to one or more electric beam deflectors, one or more acoustic beam deflectors, one or more Galvanometer scanner, one or more resonance scanners or one or more polygon scanners. In this way, the surface of the sample 110 can be scanned in an r-θ pattern. It should be further noted that the illumination beam 504 can be scanned according to any pattern on the sample. In one embodiment, the illumination beam 504 is divided into one or more beams so that one or more beams can be scanned simultaneously.

In another embodiment, the detection subsystem 102 includes one or more detectors 522 (eg, one or more optical detectors, one or more photon detectors, or the like). In order to capture the radiation emitted from the sample 110 through a collection path 518. The collection path 518 may include a plurality of optical elements to guide and/or modify the illumination collected by the objective lens 516, including but not limited to one or more lenses 520, one or more filters, one or more polarizers, One or more beam stops or one or more beam splitters. It should be noted herein that the components of the collection path 518 can be oriented in any position relative to the sample 110. In one embodiment, the collection path includes an objective lens 516 oriented normal to the sample 110. In another embodiment, the collection path 518 includes a plurality of collecting lenses oriented to collect radiation from the sample at a plurality of solid angles.

In one embodiment, the detection system 100 includes a bright field detection system. For example, a bright field image of the sample 110 or a portion of the sample 110 may be projected onto the detector 522 (eg, through the objective lens 516, one or more lenses 520, or the like). In another embodiment, the detection system 100 includes a dark field detection system. For example, the detection system 100 may include one or more components (eg, an annular beam stop, a dark field objective 516, or the like) to direct the illumination beam 504 to the sample 110 at a large angle of incidence, such that the detector The image of the sample on 522 is associated with scattered and/or diffracted light. In another embodiment, the detection system 100 includes a tilt angle detection system. For example, the inspection system 100 may direct the illumination beam 504 to the sample at an off-axis angle to provide contrast for defect inspection. In another embodiment, the detection system 100 includes a phase contrast detection system. For example, the detection system 100 may include one or more phase plates and/or beam stops (eg, an annular beam stop or the like) to provide a phase contrast between diffracted and undiffracted light from the sample, Thereby providing a comparison for defect detection. In another embodiment, the detection system 100 may include a luminescence detection system (eg, a fluorescence detection system, a phosphorescence detection system, or the like). For example, the detection system 100 may direct an illumination beam 504 having a first wavelength spectrum to the sample 110 and include one or more filters to detect the emission from the sample 110 (eg, from one or more of the sample 110 Components and/or one or more defects on the sample 110 emit one or more additional wavelength spectra. In another embodiment, the detection system includes one or more pinholes positioned at a confocal position such that the system 100 is operable as a confocal detection system.

5B is a simplified schematic diagram of a detection subsystem configured as a particle beam detection subsystem according to one or more embodiments of the invention. In one embodiment, the illumination source 502 includes a particle source configured to generate a particle beam 504. The particle source 502 may include any particle source known in the art to be suitable for generating a particle beam 504. By way of non-limiting example, the particle source 502 may include, but is not limited to, an electron gun or an ion gun. In another embodiment, the particle source 502 is configured to provide a particle beam 504 with a tunable energy. For example, a particle source 502 including an electron source may be (but not limited to) provided at 0.1 kV to 30 kV One of the ranges accelerates the voltage. As another example, a particle source including an ion source may (but need not) provide an ion beam having an energy value in the range of 1 keV to 50 keV.

In another embodiment, the detection subsystem 102 includes two or more particle beam sources 502 (eg, electron beam sources or ion beam sources) for generating two or more particle beams 504.

In another embodiment, the illumination path 506 includes one or more particle focusing elements 524. For example, the one or more particle focusing elements 524 may include, but are not limited to, a single particle focusing element or one or more particle focusing elements forming a composite system. In another embodiment, an objective lens 516 of the system 100 is configured to direct the particle beam 504 to the sample 110. In addition, the one or more particle focusing elements 524 and/or the objective lens 516 may include any type of particle lens known in the art, including (but not limited to) electrostatic, magnetic, unipotential or bipotential lenses. In addition, the detection subsystem 102 may include, but is not limited to, one or more electronic deflectors, one or more apertures, one or more filters, or one or more astigmatism correctors.

In another embodiment, the detection subsystem 102 includes one or more particle beam scanning elements 526. For example, the one or more particle beam scanning elements may include, but are not limited to, one or more scanning coils or deflectors adapted to control the position of the beam relative to the surface of the sample 110. In this regard, the one or more scanning elements can be used to scan the particle beam 504 across the sample 110 in a selected pattern.

In another embodiment, the detection subsystem includes a detector 522 to image or otherwise detect the particles 528 emitted from the sample 110. In one embodiment, the detector 522 includes an electron collector (eg, secondary electron collector, backscattered electron detector, or the like). In another embodiment, the detector 522 includes a photon detector (eg, a photodetector, an x-ray detector, a scintillation element coupled to a photomultiplier tube (PMT) detector, or the like) Used to detect electrons and/or photons from the sample surface. Generally speaking, it should be recognized herein that the detector 522 may include any device or combination of devices known in the art for characterizing a sample surface or block using a particle beam 504. E.g, The detector 522 may include known in the art configured to collect backscattered electrons, Auger electrons, transmitted electrons or photons (e.g., x-rays emitted by the surface in response to incident electrons, Any particle detector of cathode luminescence of sample 110 or the like).

In another embodiment, the detection system 100 includes a voltage contrast imaging (VCI) system. It should be recognized herein that detection systems using particle beams (eg, electron beams, ion beams, or the like) can be attributed to a high achievable spatial resolution for detecting and/or identifying a semiconductor sample (eg, a random Defective mechanisms on logic chips or the like are particularly useful. For example, a particle beam can be used in a detection system to image a sample (for example, by capturing secondary electrons emitted from a sample, backscattered electrons, or the like). In addition, structures on a sample (for example, a patterned semiconductor wafer) can exhibit a charging effect in response to excitation using a particle beam. The charging effect may include a modification of the number of electrons (eg, secondary electrons) extracted by the system and therefore the VCI signal strength. In this regard, a voltage contrast imaging (VCI) system can produce a high-resolution image of a sample, where the intensity of each pixel of the image provides information about the electrical properties of the sample at the pixel location. For example, an insulating structure and/or a structure that is not connected to a grounded source (eg, ungrounded) may generate a charge (eg, a positive charge or a negative charge) in response to particle depletion caused by the particle beam. Therefore, the induced charge can deflect the orbit of the secondary electrons and reduce the signal intensity captured by a detector. Conversely, the ground structure may not generate a charge and therefore may exhibit a strong signal (eg, appear bright in an associated VCI image). In addition, the signal strength of the capacitive structure may vary according to the scanning speed and/or energy of the particle beam. In this regard, a VCI image may include a grayscale image, where the grayscale value of each pixel provides information about the relevant electrical characteristics of the location on the wafer. In a further embodiment, the detection system 100 includes one or more components (eg, one or more electrodes) configured to apply one or more voltages to one or more locations of the sample 110. In this regard, the system 100 can generate effective voltage contrast imaging data.

In another embodiment, the detection system 100 may include a display (not shown). In another In one embodiment, the display is communicatively coupled to the controller 104. For example, the display may be communicatively coupled to one or more processors 106 of the controller 104. In this regard, one or more processors 106 may display one or more of the various results of the present invention on a display.

The display device may include any display device known in the art. In one embodiment, the display device may include, but is not limited to, a liquid crystal display (LCD). In another embodiment, the display device may include, but is not limited to, an organic light emitting diode (OLED) based display. In another embodiment, the display device may include (but is not limited to) a CRT display. Those skilled in the art should recognize that various display devices may be suitable for implementation in the present invention and the specific selection of display devices may depend on various factors, including but not limited to appearance size, cost, and the like. In a general sense, any display device that can be integrated with a user interface device (eg, touch screen, panel mounting interface, keyboard, mouse, track pad, and the like) is suitable for implementation in the present invention.

In another embodiment, the detection system 100 may include a user interface device (not shown). In one embodiment, the user interface device is communicatively coupled to one or more processors 106 of the controller 104. In another embodiment, the user interface device can be used by the controller 104 to accept selections and/or commands from a user. In some embodiments described further herein, the display can be used to display data to a user. Then, a user can input selections and/or commands in response to the detection data displayed to the user through the display device (eg, user selection of one of the detection areas).

The user interface device may include any user interface known in the art. For example, the user interface may include (but not limited to): keyboard, keypad, touch screen, lever, knob, scroll wheel, trackball, switch, dial, slider, scroll bar, slider, handle, touch Pad, pedal, steering wheel, joystick, panel input device or the like. In the case of a touch screen interface device, those skilled in the art should realize that a large number of touch screen interface devices can be suitable for implementation in the present invention. For example, the display device may interface with a touch screen (such as but not limited to a capacitive touch screen, a resistive touch screen, a table-based Surface acoustic wave touch screen, an infrared-based touch screen or the like) are integrated. In a general sense, any touch screen interface that can be integrated with the display portion of the display device 105 is suitable for implementation in the present invention. In another embodiment, the user interface may include (but is not limited to) a panel mounting interface.

It should be noted herein that FIGS. 5A and 5B and the corresponding description above are provided for illustration only and should not be construed as limiting. It is expected that many equivalent or additional configurations can be utilized within the scope of the present invention.

The subject matter described herein sometimes illustrates different components contained within or connected to other components. It should be understood that these depicted architectures are merely exemplary, and in fact many other architectures that achieve the same functionality can be implemented. In a conceptual sense, any configuration of components that achieve the same functionality is effectively "associated" to achieve the desired functionality. Therefore, any two components that are combined herein to achieve a particular functionality may be considered "associated" with each other, such that the desired functionality is achieved, regardless of architecture or intermediate components. Likewise, any two components so associated can also be regarded as "connected" or "coupled" to each other to achieve the desired functionality, and any two components that can be so associated can also be regarded as "coupleable" to To achieve the desired functionality. Specific examples that may be coupled include, but are not limited to, physical and/or physical interactive components and/or wireless interactive and/or wireless interactive components and/or logical interactive and/or logical interactive components.

It is believed that the present invention and its many accompanying advantages will be understood from the foregoing description, and it will be understood that various changes can be made in the form, configuration and configuration of components without departing from the disclosed subject matter or without sacrificing all of its material advantages. The form of description is merely illustrative, and the following patent application is intended to cover and include such changes. In addition, it should be understood that the present invention is defined by the scope of the accompanying patent application.

102‧‧‧ Detection subsystem

104‧‧‧Controller

106‧‧‧ processor

108‧‧‧Memory media/memory/memory device

110‧‧‧ sample

Claims (47)

A defect detection system includes: a detection subsystem including: an illumination source configured to generate an illumination beam; a set of illumination optics for guiding the illumination beam to a sample; and a A detector, which is configured to collect the illumination emitted from the sample; and a controller, which is communicatively coupled to the detector, the controller includes a memory device and is configured to execute multiple program instructions One or more processors, the program instructions are configured to cause the one or more processors to store a design data of the same copy in the memory device of the controller, wherein the design data is preprocessed to include A searchable data set, which is suitable for identifying a plurality of patterns of interest in the design data, wherein the design data is registered to the detection subsystem to provide the identified ones of the coordinates of the detection subsystem Multiple positions of multiple patterns of interest; determining one or more target patterns as multiple patterns of interest corresponding to one or more features on the sample; based on identifying the stored in the memory device of the controller Multiple instances of the one or more target patterns in the design data to define one or more care areas on the sample; and during the detection of the one or more care areas using the lighting subsystem, based on The illumination collected by the detector identifies one or more defects in the one or more care areas of the sample. The defect detection system of claim 1, wherein the one or more instances identifying the one or more target patterns in the design data of the sample include: generating a compound search pattern, wherein the compound search pattern includes a source Pattern and At least one target pattern of the one or more target patterns; identifying one or more instances of the composite pattern in the design data of the sample; and based on the identified one or more instances of the composite pattern Identify the one or more instances of the one or more target patterns in the design data of the sample. The defect detection system of claim 1, wherein the examples of identifying the one or more target patterns in the design data of the sample include: searching the design data for the one or more target patterns to generate the one or One or more identified instances of multiple target patterns. As in the defect detection system of claim 1, wherein defining the one or more care areas includes: identifying the instances of the one or more target patterns in the design data; for the one or more in the design data Each instance of the target pattern determines a reliability score between the one or more target patterns and the identified instances of the one or more target patterns in the design data A measurement of similarity; and defining a plurality of instances of the one or more target patterns in the design data having a confidence score higher than a selected confidence score as the one or more Care area. The defect detection system of claim 1, wherein defining the one or more care areas on the sample includes: defining one or more target areas, wherein the one or more target areas include at least one of the one or more target patterns At least one example of one, wherein the care areas include the one or more target areas; and a detection sensitivity is defined for each of the one or more target areas. As in the defect detection system of claim 5, the detection sensitivity of each of the one or more target areas can be adjusted individually. The defect detection system of claim 1, wherein the one or more target patterns include: one or more target patterns identified by at least one of a design-based classification or a design-based binning procedure . The defect detection system of claim 1, wherein the one or more target patterns include: one or more target patterns associated with one or more known defect types. The defect detection system of claim 1, wherein the one or more target patterns include: one or more target patterns identified by a previous defect detection procedure. The defect detection system of claim 1, wherein determining the one or more target patterns comprises: determining the one or more target patterns by a user. The defect detection system of claim 10, wherein determining the one or more target patterns by the user includes: selecting one or more instances of the one or more target patterns from the design data of the sample. The defect detection system of claim 11, wherein the one or more instances of selecting the one or more target patterns from the design data of the sample include: selecting the one or from a visual display of the design data of the sample The one or more instances of multiple target patterns. The defect detection system of claim 1, wherein defining the one or more care areas includes: determining one or more associated with one or more instances of the one or more target patterns in the design data of the sample Coordinates. The defect detection system of claim 1, wherein the one or more processors are further configured to execute a plurality of program instructions that cause the one or more processors to perform the following: based on the identified one or more defects Determine one or more additional target patterns as multiple patterns of interest; Define one or more additional care areas on the sample based on the instances identifying the one or more additional target patterns stored in the design data stored in the memory device of the controller; and based on using a The virtual inspection system detects and identifies one or more additional defects in the one or more additional care areas. The defect detection system of claim 1, wherein the design information includes: at least one of a physical layout of the sample or an electrical layout of the sample. The defect detection system of claim 1, further comprising: providing the one or more care areas for use in a subsequent inspection procedure. The defect detection system of claim 1, further comprising: classifying one or more identified defects based on the design data of the sample. The defect detection system of claim 1, wherein the illumination beam includes: at least one of a photon beam or a particle beam. The defect detection system of claim 18, wherein the particle beam includes: at least one of an electron beam or an ion beam. The defect detection system of claim 1, wherein the set of illumination optics includes at least one of photonic optics or particle optics. The defect detection system of claim 1, wherein the detector includes: at least one of a photon detector or a particle detector. The defect inspection system of claim 1, wherein the controller is located close to the inspection subsystem. The defect inspection system of claim 1, wherein at least a portion of the controller and the inspection subsystem are located in a common housing. The defect detection system of claim 1, wherein determining the one or more target patterns includes: providing one of the one or more target patterns in the design data of the sample or One or more coordinates associated with multiple instances. The defect detection system of claim 1, wherein the one or more processors are further configured to execute a plurality of program instructions that cause the one or more processors to perform the following: based on the identified one or more defects Determine one or more additional target patterns as a plurality of patterns of interest; define the one based on a plurality of instances of the one or more additional target patterns identified in the design data stored in the memory device of the controller One or more additional care areas on the sample; and during the detection of the one or more additional care areas using the lighting subsystem, identifying the one or more additional care areas based on the illumination collected by the detector One or more additional defects. The defect inspection system of claim 1, wherein the inspection system is a virtual inspection system. A defect detection system includes: a detection subsystem including: an illumination source configured to generate an illumination beam; a set of illumination optics that directs the illumination beam to a sample; a detector , Which is configured to collect the lighting emitted from the sample; and a controller, which is communicatively coupled to the detector, the controller includes a memory device and is configured to execute one or more program instructions Processors, the program instructions are configured to cause the one or more processors to: determine one or more target patterns corresponding to one or more features on the sample; determine a source pattern, wherein the source pattern A subset of multiple instances of the one or more target patterns within the design data of the sample, where the design data of the sample is stored in the memory device of the controller; Define a spatial relationship between the source pattern and the at least one target pattern of the subset of the instances of the one or more target patterns in the design data of the sample; identify the design data of the sample One or more instances of the source pattern; based on the identified one or more instances of the source pattern and the subset of the instances of the source pattern and the one or more target patterns The spatial relationship between the at least one target pattern to identify the subset of the instances of the one or more target patterns in the design data of the sample; based on the ones of the one or more target patterns The subset of the examples to define one or more care areas on the sample; and identify one or more defects in the one or more care areas of the sample based on the illumination collected by the detector . A defect detection method, comprising: storing the same design data in a memory device of a testing system, wherein the design data is preprocessed to include a searchable data set, which is suitable for identifying the design data Multiple patterns of interest, wherein the design data is registered to provide multiple coordinates of the identified multiple patterns of interest among the multiple coordinates of the detection subsystem; one or more processors of the detection system are used to determine one or Multiple target patterns are multiple patterns of interest corresponding to one or more features on the sample; using the one or more processors of the detection system, based on identifying the one or more within the design data of the sample Multiple instances of a target pattern by the detection system to define one or more care areas on the sample; by illuminating the one or more care areas with an illumination beam and using a detector collected from the sample The lighting emitted to detect the one or more care areas of the sample using the detection system; and Based on the illumination collected by the detector, the one or more processors of the detection system are used to identify one or more defects in the one or more care areas of the sample. The defect detection method of claim 28, wherein defining the one or more care areas includes: determining a source pattern, wherein the source pattern is close to multiple instances of the one or more target patterns in the design data of the sample A subset; a spatial relationship defined between the source pattern and the at least one target pattern of the subset of the instances of the one or more target patterns in the design data of the sample; identifying the sample One or more instances of the source pattern in the design data; and the one or more instances identified based on the source pattern and the one or more in the design data of the source pattern and the sample The spatial relationship between the at least one target pattern of the subset of the plurality of target patterns to identify the ones of the instances of the one or more target patterns in the design data of the sample Subset. The defect detection method of claim 28, wherein the examples of identifying the one or more target patterns in the design data of the sample include: searching the design data for the one or more target patterns to generate the one or The identified instances of multiple target patterns. The defect detection method of claim 30, wherein defining the one or more care areas includes: identifying the instances of the one or more target patterns in the design data; for the one or more in the design data Each instance of the target pattern determines a credibility score that is the identified one or more instances of the one or more target patterns in the one or more target patterns and the design data A measure of similarity between items; and A plurality of instances of the one or more target patterns in the design data having a credibility score higher than a selected credibility score are defined as the one or more care areas. The defect detection method according to claim 28, wherein the one or more target patterns include: one or more target patterns identified by at least one of a design-based classification or a design-based merging process. The defect detection method of claim 28, wherein the one or more target patterns include: one or more target patterns identified by a previous defect detection procedure. The defect detection method of claim 28, wherein determining the one or more target patterns includes: determining the one or more target patterns by a user. The defect detection method of claim 34, wherein determining the one or more target patterns by the user includes selecting one or more instances of the one or more target patterns from the design data of the sample. The defect detection method of claim 35, wherein the one or more instances of selecting the one or more target patterns from the design data of the sample include: selecting the one or from a visual display of the design data of the sample The one or more instances of multiple target patterns. The defect detection method of claim 33, further comprising: determining one or more additional target patterns as a plurality of patterns of interest based on the identified one or more defects; based on identifying the memory stored in the controller Multiple instances of the one or more additional target patterns in the design data in the device to define one or more additional care areas on the sample; and identifying the one or more based on detection using a virtual detection system Extra care One or more additional defects in the area. The defect detection method of claim 33, wherein the design information includes: at least one of a physical layout of the sample or an electrical layout of the sample. The defect detection method of claim 33, further comprising: providing the one or more care areas for use in a subsequent inspection procedure. The defect detection method of claim 28 further includes: classifying one or more identified defects based on the design data of the sample. A defect detection system includes: a detection subsystem including: an illumination source configured to generate an illumination beam; a set of illumination optics used to guide the illumination beam to a sample; a detection A detector, which is configured to collect the lighting emitted from the sample; and a controller, which is communicatively coupled to the detector, the controller includes a memory device and is configured to execute one of a plurality of program instructions Or multiple processors, the program instructions are configured to cause the one or more processors to: determine one or more target patterns as multiple patterns of interest corresponding to one or more features on the sample; generate A composite search pattern, wherein the composite search pattern includes a source pattern and at least one target pattern of the one or more target patterns; based on identifying one or more instances of the composite search pattern in the design data of the sample Define one or more care areas on the sample, wherein the design data of the sample is stored in the memory device of the controller; and during the detection of the one or more care areas using the lighting subsystem, based on The illumination collected by the detector identifies one or more defects in the one or more care areas of the sample. For the defect inspection system of claim 41, in which the design data identifying the sample is included The instances of the one or more target patterns include: searching the design data for the one or more target patterns to generate the identified one or more instances of the one or more target patterns. The defect detection system of claim 41, wherein defining the one or more care areas includes: identifying the instances of the one or more target patterns in the design data; for the one or more in the design data Each instance of the target pattern determines a reliability score between the one or more target patterns and the identified instances of the one or more target patterns in the design data A measurement of similarity; and defining a plurality of instances of the one or more target patterns in the design data having a confidence score higher than a selected confidence score as the one or more Care area. The defect detection system of claim 41, wherein the one or more target patterns include: one or more target patterns identified by a previous defect detection procedure. The defect detection system of claim 41, wherein determining the one or more target patterns comprises: determining the one or more target patterns by a user. The defect detection system of claim 45, wherein determining the one or more target patterns by the user includes selecting one or more instances of the one or more target patterns from the design data of the sample. The defect detection system of claim 46, wherein the one or more instances of selecting the one or more target patterns from the design data of the sample include: selecting the one or from a visual display of the design data of the sample The one or more instances of multiple target patterns.

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