JP2015188018A - Analysis support method and analysis support program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an analysis support method and an analysis support program that can shorten the time for identifying a failure factor.SOLUTION: An analysis support device 100 derives a first feature value W1 based on an exposure simulation of a transfer pattern tp1 of a predetermined layer of each of plural first cells c1n under exposure of a predetermined exposure condition ec by using a layout pattern lp1 of a predetermined layer of the plural first cells c1n, and a second feature value W2 based on exposure simulation of a transfer pattern tp2 of a predetermined layer of each of plural second cells c2m under exposure of a predetermined exposure condition ec by using a layout pattern lp2 of a predetermined layer of the plural second cells c2m. The analysis support device 100 determines the presence or absence of a significant difference on the basis of an index value iv representing the significant difference between the first feature value derived for each of the plural first cells c1n and the second feature value W2 derived for each of the plural second cells c2m.

Description

  The present invention relates to an analysis support method and an analysis support program.

  Conventionally, there is a technique for identifying a cause of a failure when a failure is detected in a test of a semiconductor integrated circuit. Conventionally, a singularity map extracted from a distribution map of QC (Quality Control) data of each process associated with the manufacture of a semiconductor integrated circuit is created, and the cause of the defect is based on a comparison between the singularity map and the defect occurrence map A technique for estimating the process is known (for example, refer to Patent Document 1 below).

  Conventionally, a technique is known in which a local pattern to be evaluated is determined by performing exposure simulation using the entire layout pattern of a semiconductor integrated circuit and creating a risk map on the layout pattern ( For example, refer to Patent Document 2 below).

  Conventionally, a failure caused by exposure is specified by, for example, an analysis performed by physically peeling or a cross-sectional analysis performed by physically cutting.

JP 2008-109095 A JP 2009-105292 A

  However, for example, when a physical analysis is performed to identify a failure factor due to exposure, there is a problem that it takes time to identify the failure factor.

  In one aspect, an object of the present invention is to provide an analysis support method and an analysis support program that can shorten the time taken to identify a failure factor.

  According to an aspect of the present invention, each of the plurality of first cells in the plurality of first cells in the case where exposure is performed under a predetermined exposure condition using a layout pattern of the plurality of first cells included in the target circuit. A first feature value based on an exposure simulation of a transfer pattern is derived, and a layout pattern of the predetermined layer of a plurality of second cells different from the plurality of first cells included in the target circuit is used according to the predetermined exposure condition. Deriving a second feature value based on an exposure simulation of the transfer pattern of the predetermined layer of each of the plurality of second cells when exposed, the first feature value derived for each of the plurality of first cells; An analysis support method for calculating an index value indicating a significant difference between the second feature value derived for each of the plurality of second cells, and an analysis support program There is proposed.

  According to one embodiment of the present invention, it is possible to shorten the time taken to specify a failure caused by exposure.

FIG. 1 is an explanatory diagram showing an operation example by the analysis support apparatus according to the present invention. FIG. 2 is an explanatory view showing an example of a laminated structure. FIG. 3 is an explanatory diagram showing examples of line width and extraction width. FIG. 4 is an explanatory diagram illustrating a system example. FIG. 5 is a block diagram illustrating a hardware configuration example of the analysis support apparatus. FIG. 6 is a block diagram illustrating a functional configuration example of the analysis support apparatus. FIG. 7 is an explanatory diagram illustrating an example of a failure candidate cell list. FIG. 8 is an explanatory diagram (part 1) showing the output result of the exposure simulation. FIG. 9 is an explanatory diagram (part 2) of the output result of the exposure simulation. FIG. 10 is an explanatory diagram showing an example of a table of exposure simulation results. FIG. 11 is an explanatory diagram illustrating an example of a list of simulation results according to simulation conditions. FIG. 12 is an explanatory diagram illustrating a group example. FIG. 13 is an explanatory diagram showing an example of ranking based on the index value. FIG. 14 is a flowchart illustrating an example of an analysis support processing procedure performed by the analysis support apparatus. FIG. 15 is a flowchart showing a detailed description of the abnormal cell specifying process shown in FIG. FIG. 16 is a flowchart showing a detailed description of the process of creating the exposure simulation table shown in FIG. FIG. 17 is a flowchart showing in detail the exposure failure factor analysis process shown in FIG.

  Exemplary embodiments of an analysis support method and an analysis support program according to the present invention will be described below in detail with reference to the accompanying drawings.

  FIG. 1 is an explanatory diagram showing an operation example by the analysis support apparatus according to the present invention. The analysis support apparatus 100 is a computer that supports the identification of a failure. For example, the analysis support apparatus 100 assists in specifying whether or not the failure is caused by exposure, which layer is caused by exposure, and which exposure condition ec is caused by exposure. To do.

  As shown in FIG. 1A, the analysis support apparatus 100 derives a first feature value W1 based on an exposure simulation of a transfer pattern tp of a predetermined layer of each of the plurality of first cells c1n included in the target circuit. The transfer pattern is a transfer pattern when the exposure is performed under a predetermined exposure condition ec using the layout pattern lp of the predetermined layer of the plurality of first cells c1n. Further, as shown in FIG. 1B, the analysis support apparatus 100 performs an exposure simulation of the transfer pattern tp of the predetermined layer of each of the plurality of second cells c2m that is included in the target circuit and is different from the plurality of first cells c1n. A second feature value W2 is derived. The transfer pattern tp is a transfer pattern in the case where exposure is performed under a predetermined exposure condition ec using a layout pattern lp of a predetermined layer of the plurality of second cells c2m.

  The plurality of first cells c1n are cells estimated to be abnormal cells among the plurality of cells included in the target circuit, for example. The abnormal cell may be specified by the user, for example, or may be specified based on a failure diagnosis result as described later in the present embodiment. In the example of FIG. 1, the first cell c11 is a two-input negative OR (OR) cell, and the first cell c12 is a negative (INVERTER (abbreviated as “INV” hereinafter)) cell. It is. The plurality of second cells c2m are, for example, normal cells that are not abnormal cells among the plurality of cells included in the target circuit. For example, the normal cells may be designated by the user, or all cells other than the abnormal cells may be set as normal cells. In the example of FIG. 1, a two-input NAND (AND) cell is exemplified as the second cell c21, and a buffer cell is exemplified as the second cell c22.

  The predetermined layer is, for example, a layer to be exposed, and a detailed layer example will be described with reference to FIG. The layout pattern lp is a pattern designed in layout design, and is defined by layout data. In the example of FIG. 1, a layout pattern lp1n of a plurality of first cells c1n (n> 1) and a layout pattern lp2m of a plurality of second cells c2m (m> 1) are shown. For example, the layout pattern of the predetermined layer of the first cell c11 is the layout pattern lp11, and the layout pattern of the predetermined layer of the first cell c12 is the layout pattern lp12. For example, the layout pattern of the predetermined layer of the second cell c21 is the layout pattern lp21, and the layout pattern of the predetermined layer of the second cell c22 is the layout pattern lp22.

  The predetermined exposure condition ec includes, for example, the intensity of light such as the lens height and exposure amount during exposure. In the example of FIG. 1, there are a case where the lens is 10 [nm] higher than the specified lens height as the focus and a case where the exposure amount is 10% higher than the specified exposure amount. The exposure condition is ec. In the exposure simulation, it is possible to simulate the exposure based on the exposure condition ec using the layout pattern lp.

  The transfer pattern tp is a pattern transferred when exposed using the layout pattern lp. In the example of FIG. 1, a transfer pattern tp1n of a plurality of first cells c1n and a transfer pattern tp2m of a plurality of second cells c2m are shown. For example, the transfer pattern for the first cell c11 is the transfer pattern tp11, and the transfer pattern for the first cell c12 is the transfer pattern tp12. For example, the transfer pattern for the second cell c21 is the transfer pattern tp21, and the transfer pattern for the second cell c22 is the transfer pattern tp22.

  The first feature value W1 and the second feature value W2 are values that can determine a failure, for example. For example, details of an example of a failure are shown in FIG. In the example of FIG. 1, the minimum wiring width of the transfer pattern tp is set to the first feature value W1 and the second feature value W2. In the example of FIG. 1, a plurality of first feature values W1n for a plurality of first cells c1n and a plurality of second feature values W2m for a plurality of second cells c2m are shown. For example, the first feature value for the first cell c11 is the first feature value W11, and the first feature value for the first cell c12 is the first feature value W12. For example, the second feature value for the second cell c21 is the second feature value W21, and the second feature value for the second cell c22 is the second feature value W22.

  The analysis support apparatus 100 may sequentially perform the derivation of the first feature value W1 for each of the first cells c1 and the derivation of the second feature value W2 for each of the second cells c2. Alternatively, the analysis support apparatus 100 may group the first feature value W1 and the second feature value W2 after deriving the feature values for all the cells included in the target circuit.

  As shown in FIG. 1 (3), the analysis support apparatus 100 includes a first feature value W1 derived for each of the plurality of first cells c1n and a second feature value W2 derived for each of the plurality of second cells c2m. And an index value iv indicating a significant difference between them. As index value iv which shows a significant difference, the difference of p value, an average value, etc. are mentioned, for example. For example, the p value is obtained by using the first feature value W1 when actually exposed using the layout pattern lp1 based on the derived first feature value W1 and the derived second feature value W2, and the layout pattern lp2. This is the probability that there is no difference between the second feature value W2 and the actual exposure value. The difference between the average values is, for example, a difference between the derived average value of the first feature value W1 and the derived average value of the second feature value W2.

  In the example of FIG. 1 (4), the index value iv is set to the p value. The analysis support apparatus 100 determines whether there is a significant difference based on the calculated index value iv. For example, the analysis support apparatus 100 determines whether there is a significant difference by comparing the index value iv with the threshold value th. For example, the threshold th is predetermined by the user and stored in a storage device accessible by the analysis support apparatus 100. In the example of FIG. 1 (4), the threshold th is 0.05. In other words, it is determined whether or not the probability that there is no difference between the first feature value W1 and the second feature value W2 is 5 [%] or more. If it is 5% or more, it is determined that there is a high probability that there is no difference between the first feature value W1 and the second feature value W2, and if it is less than 5%, the first feature value W1 and the first feature value W1 are the same. It is determined that the probability that there is no difference between the two feature values W2 is low. Therefore, in the example of FIG. 1 (4), the analysis support apparatus 100 determines that there is no significant difference if the index value iv is equal to or greater than the threshold th, and if the index value iv is less than the threshold th, the significant difference is determined. Judge that there is. In the example of FIG. 1 (4), the analysis support apparatus 100 determines that there is a significant difference.

  For example, when the index value iv is a difference between the average values, for example, the control unit determines whether or not there is a significant difference depending on whether or not the index value iv is equal to or greater than the threshold th. For example, when the index value iv is greater than or equal to the threshold th, the control unit determines that there is a significant difference. For example, when the index value iv is less than the threshold th, the control unit determines that there is no significant difference. For example, the threshold th is determined in advance by the user and stored in a storage unit accessible to the analysis support apparatus 100.

  In this way, the analysis support apparatus 100 derives the characteristic value of the transfer pattern tp of the same layer for each of the abnormal cell group and the normal cell group by exposure simulation under the same exposure condition ec, and between the characteristic values. Determine significant difference. Thereby, it is determined whether there is a possibility that a failure has occurred in the combination of the layer and the exposure condition ec.

  For example, when it is determined that there is no significant difference, it can be determined that no failure has occurred in the combination of the layer and the exposure condition ec. Therefore, for example, when a failure factor analyst performs a physical analysis in the combination, the failure factor analyst can make a determination such as lowering the priority of the combination. The physical analysis is a physical cross-sectional analysis or a physical separation analysis. Also, for example, when it is determined that there is a significant difference, it is determined that a failure may have occurred in the combination of the cell, the layer, and the exposure condition ec, and the failure factor analyst is based on the combination. And physical analysis may be performed. In this way, it is possible to shorten the time required for failure identification. Further, since the exposure simulation is performed in units of cells, the time required for the exposure simulation can be shortened compared with the case where the simulation is performed on the entire target circuit. Further, since the same type of cells may be subjected to a single exposure simulation, the time required for the exposure simulation can be reduced.

  FIG. 2 is an explanatory view showing an example of a laminated structure. FIG. 2 is a schematic cross-sectional view of the laminated structure of the wiring in the semiconductor device. A transistor included in the cell is formed by an active layer, a poly layer, a metal layer of wiring, and a contact layer and a via layer that connect the respective layers. The Active layer is an n-channel or p-channel active layer. Examples of the metal layer of the wiring include a metal 1 layer, a metal 2 layer, and a metal 3 layer. Examples of the Via layer include a Via1 layer and a Via2 layer.

  The Contact in the Contact layer connects, for example, the wiring in the Poly layer and the wiring in the Metal1 layer. Via of the Via1 layer connects, for example, a wiring of the Metal1 layer and a wiring of the Metal2 layer. Via of the Via2 layer connects, for example, a wiring of the Metal2 layer and a wiring of the Metal3 layer. In the present embodiment, the active layer, the poly layer, the metal layer, and the like are the analysis targets of the failure factor as the layers to be exposed.

  FIG. 3 is an explanatory diagram showing examples of line width and extraction width. FIG. 3 shows the layout pattern lp, the transfer pattern tp, and the characteristic value of the transfer pattern tp. The layout pattern lp is a pattern designed by layout design. The transfer pattern tp is a pattern based on an exposure simulation under a predetermined exposure condition ec using the layout pattern lp. The exposure condition ec will be described later. Examples of the characteristic value of the transfer pattern tp include a wiring width and a removal width. The wiring width is the width of the wiring. If the wiring width is too small, a failure occurs in which the wiring is disconnected. Therefore, the smallest wiring width having the smallest wiring width is the characteristic value of the transfer pattern tp. The failure due to the wiring width is also referred to as “Necking”. Further, the extraction width is a distance between the wiring patterns. When the extraction width is narrowed, a failure occurs in which the wirings are stuck together. Therefore, the minimum extraction width with the smallest extraction width is the characteristic value of the transfer pattern tp. Become. A failure due to a drawing width is also referred to as bridging.

  FIG. 4 is an explanatory diagram illustrating a system example. The system 400 includes a tester 401 that can test a wafer having a plurality of target circuits, and the analysis support apparatus 100. The tester 401 and the analysis support apparatus 100 are connected via a network NET. The analysis support apparatus 100 includes, for example, cell data 412, circuit information 413, a test pattern 414, a table 415 that is an exposure simulation result, and the like. The tester 401 can test a wafer having a plurality of target circuits, and has failure data 411 when a failure is detected. Further, it may be stored in a storage device included in the analysis support apparatus 100. The analysis support apparatus 100 performs processing such as specification of an abnormal cell by failure diagnosis, exposure simulation, and failure factor analysis.

(Hardware configuration example of the analysis support apparatus 100)
FIG. 5 is a block diagram illustrating a hardware configuration example of the analysis support apparatus. In FIG. 5, the analysis support apparatus 100 includes a CPU (Central Processing Unit) 501, a ROM (Read Only Memory) 502, a RAM (Random Access Memory) 503, a disk drive 504, and a disk 505. The analysis support apparatus 100 includes an I / F (Inter / Face) 506, an input device 507, and an output device 508. Each unit is connected by a bus 500.

  Here, the CPU 501 governs overall control of the analysis support apparatus 100. The ROM 502 stores a program such as a boot program. The RAM 503 is used as a work area for the CPU 501. The disk drive 504 controls reading / writing of data with respect to the disk 505 according to the control of the CPU 501. The disk 505 stores data written under the control of the disk drive 504. Examples of the disk 505 include a magnetic disk and an optical disk.

  The I / F 506 is connected to a network NET such as a LAN (Local Area Network), a WAN (Wide Area Network), and the Internet through a communication line, and is connected to other devices via the network NET. The I / F 506 controls an internal interface with the network NET, and controls data input / output from an external device. For example, a modem or a LAN adapter may be employed as the I / F 506.

  The input device 507 is an interface for inputting various data by a user operation such as a keyboard, a mouse, and a touch panel. The input device 507 can also capture images and moving images from the camera. In addition, the input device 507 can capture sound from a microphone. The output device 508 is an interface that outputs data in accordance with an instruction from the CPU 501. Examples of the output device 508 include a display and a printer.

(Functional configuration example of the analysis support apparatus 100)
FIG. 6 is a block diagram illustrating a functional configuration example of the analysis support apparatus. The analysis support apparatus 100 includes a control unit 601 and a storage unit 602. The storage unit 602 is realized by the ROM 502, the RAM 503, the disk 505, and the like. The processing of the control unit 601 is coded in an analysis support program stored in a storage device accessible by the CPU 501, for example. Then, the CPU 501 reads the analysis support program from the storage device, and executes the process coded in the analysis support program. Thereby, the processing of the control unit 601 is realized. The processing result of the control unit 601 is stored in a storage device such as the RAM 503 and the disk 505, for example.

  The storage unit 602 includes, for example, cell data 412, circuit information 413, and a test pattern 414. The cell data 412 is layout data indicating a cell for each cell included in the target circuit. The cell data 412 shows a layout pattern lp for each layer forming the cell shown in FIG. The cell data 412 is data prepared for each cell type, for example. The cell type is determined by, for example, a combination of performance and calculation function. For example, even if the calculation type is the same, two cells having different numbers of terminals have different calculation functions, so the two cells are different types of cells. In addition, even if the calculation function is the same, performance such as the capability of the transistors included in the cell may be different, so two cells having different performance are different types of cells.

  The circuit information 413 is a net list indicating the target circuit. The circuit information 413 is described by, for example, a hardware description language. The test pattern 414 is information indicating a signal that can be input to an input terminal included in the target circuit using the tester 401. By giving a test pattern 414 to the target circuit using the tester 401, it is detected whether or not a desired failure has occurred in the target circuit. Further, a plurality of test patterns 414 may be provided.

  In addition, the storage unit 602 includes a table 611, a table 415, a list 612, and a ranking result 613 created by the analysis support apparatus 100. Each information generated by the analysis support apparatus 100 will be described later.

  Further, the processing by the control unit 601 is roughly divided into, for example, <1> processing for identifying an abnormal cell, <2> processing for creating an exposure simulation table, and <3> processing for analyzing an exposure failure factor. It can be classified into processing. <1> The process of specifying an abnormal cell is a process of specifying the plurality of first cells c1n described with reference to FIG. <2> The process of creating the exposure simulation table is a process of deriving each feature value described in FIG. <3> The process of analyzing the cause of exposure failure is a process of determining the presence or absence of a significant difference based on the index value iv indicating the significant difference described with reference to FIG.

  First, the process of identifying <1> abnormal cells by the control unit 601 will be described in detail. The control unit 601 receives a position of a failure candidate based on a signal output from an output terminal included in the target circuit when a specific signal is input to the input terminal included in the target circuit and a failure occurs in the target circuit. A cell corresponding to each of the above is specified. The specific signal is, for example, a test pattern 414. The output signal is failure data 411. Here, the identified cell is also referred to as a failure candidate cell.

  For example, the control unit 601 acquires failure data 411 from the tester 401 when it is determined that there is a failure when the test pattern 414 is given. For example, the control unit 601 identifies the position of the failure candidate by executing failure diagnosis using the failure data 411, the circuit information 413, and the test pattern 414. Fault diagnosis is a technique for logically narrowing down the positions of fault candidates by reproducing the operation of a target circuit on a computer based on circuit information 413, test pattern 414, fault data 411, and the like. For example, the position of the failure candidate is estimated in units of nets connecting the cells, and therefore the position of the failure candidate is also referred to as a failure estimation net, for example.

  For example, the control unit 601 specifies a fault estimation net when the fault diagnosis has high estimation accuracy and is narrowed down to a single net or a small number of nets. The high estimation accuracy is, for example, a case where diagnosis can be made to the extent that it is possible to determine that a disconnection has occurred in units of wiring. Then, the control unit 601 specifies a cell connected to the specified failure estimation net. The cell connected to the failure estimation net here is a cell corresponding to the position of the failure candidate. Here, the identified cell is also referred to as a failure candidate cell.

  Further, when a failure is detected in an SRAM (Static RAM) macro, when testing is performed for each SRAM macro by an individual test mechanism, the control unit 601 performs failure candidate SRAM macro in units of the test pattern 414 in which the failure is detected. Is identified. The test mechanism here is, for example, a memory BIST (Build-In Self Test) circuit.

  Further, for example, when a plurality of SRAM macros are tested by a common test mechanism, the control unit 601 determines a failure candidate SRAM macro from the failure test terminal, the number of failure cycles of the test pattern 414, and the like together with the test pattern 414 in which the failure is detected. Is identified. The test mechanism here is, for example, a memory BIST circuit. When a failure candidate SRAM macro is specified, the control unit 601 can specify a memory cell in the failure candidate SRAM macro as a failure candidate cell by using an FBM (Fail Bit Map) test. In the FBM test, an electrical characteristic test is performed on all the memory cells using the tester 401, and the test result of the electrical characteristic test is displayed at a position corresponding to each memory cell.

  Here, for example, the plurality of identified failure candidate cells may be the abnormal cells, and may be the plurality of first cells c1n described in FIG. As a result, the abnormal cell group and the normal cell group are distinguished from each other by the actually failed target circuit. Therefore, the accuracy of failure estimation can be improved.

  In addition, the control unit 601 specifies, for each of the target circuits included in the first substrate, a cell corresponding to each of the failure candidate positions based on a signal output from an output terminal included in the target circuit. The output signal is output from the output terminal included in the target circuit when a specific signal is input to the input terminal included in the target circuit and a failure occurs in the target circuit. In addition, the control unit 601 specifies a cell corresponding to each of the failure candidate positions for each of the target circuits included in the second substrate having the same number of target circuits as the first substrate. The second substrate is a wafer, and a plurality of second substrates may be provided as in a lot unit.

  Here, the first substrate is a wafer, the second substrate is a wafer, and there may be a plurality of units such as a lot unit. Here, the first substrate is also referred to as a first wafer, and the second substrate is also referred to as a second wafer. For example, the first wafer is an abnormal wafer whose yield is lower than the mass production level. Further, for example, the second wafer is a normal wafer with a mass production yield. Also, there may be a plurality of first wafers and second wears as in lot units. A lot including the first wafer is also referred to as a first lot, and a lot including the second wafer is also referred to as a second lot. For example, the first lot is an abnormal lot whose yield is lower than the mass production level. Further, for example, the second lot is a normal lot that is a yield of mass production level. Further, when there are a plurality of first lots, it is referred to as a first lot group, and when there are a plurality of second lots, it is referred to as a second lot group.

  For example, the control unit 601 acquires failure data 411 for the first lot and failure data 411 for the second lot from the tester 401. Then, for example, the control unit 601 specifies a failure estimation net by executing failure diagnosis using the failure data 411 acquired for the first lot. The control unit 601 identifies failure candidate cells connected to the identified failure estimation net. Further, for example, the control unit 601 identifies a failure estimation net by executing failure diagnosis using the failure data 411 acquired for the second lot. The control unit 601 identifies failure candidate cells connected to the identified failure estimation net.

  Further, for example, when a failure is detected in the SRAM macro, the control unit 601 can specify a failure candidate cell as described above.

  Next, the control unit 601 tabulates the identified failure candidate cells in a failure candidate cell list. An example of a table is shown in FIG.

  FIG. 7 is an explanatory diagram illustrating an example of a failure candidate cell list. A table 611 shows a list of failure candidate cells. The table 611 has fields of lot ID, wafer ID, die #, logic cell, and SRAM macro. By setting information in each field, it is stored as a record (for example, 700-1 to 700-n).

  Identification information capable of identifying a lot is set in the lot ID field. In the wafer ID field, identification information that can identify wafers in a lot is set. In the die # field, identification information capable of identifying the die in the wafer is set.

  In the logic cell field, the number of logic cells specified as failure candidate cells is set for each type of logic cell. The logic cell type is, for example, a cell type used in a digital circuit, and is distinguished by an operation type, input / output terminal, wiring, transistor capability, and the like. For example, a two-input NOR cell and a three-input NOR cell are both negative OR operations, but are different types of cells because the number of input / output terminals is different. For example, even if the same calculation and the same input / output terminal are used, different wiring methods and transistors are handled as different types of cells. In the SRAM macro field, the number of SRAM macros identified as failure candidate SRAM macros is set for each type of SRAM macro. For example, in the record 700-1, there are one cell B and one cell D as failure candidate cells, but there is no failure candidate SRAM macro. For example, in the record 700-2, there is no failure candidate cell, but there is one SRAM macro b as the failure candidate SRAM macro.

  Further, for example, the table 611 stores a record 700 for the first lot group that is an abnormal lot group and a record 700 for the second lot group that is a normal lot group without discrimination. Therefore, the control unit 601 stores the records in the table 611 separately for the record 700 for the first lot group and the record 700 for the second lot group.

  Next, for each of a plurality of types of cells included in the target circuit, the control unit 601 includes a first number of failure candidate cells corresponding to the type for the first wafer and a failure corresponding to the type for the second wafer. A second number of candidate cells is derived. Then, the control unit 601 specifies the type based on the difference between the derived first number and the derived second number among the plurality of types. The first number and the second number are also referred to as the number of cell defects. The number of target circuits included in the first lot group and the number of target circuits included in the second lot group are also referred to as the number of target chips. For example, the control unit 601 calculates the cell defect occurrence rate for each of the first lot group and the second lot group based on the following formula (1) as follows.

  Cell defect occurrence rate = cell defect number / target chip number (1)

  Then, for example, the control unit 601 specifies a type in which the difference between the cell defect occurrence rate for the first lot group and the cell defect occurrence rate for the second lot group satisfies a predetermined condition among the plurality of types. . Alternatively, for example, the control unit 601 specifies a predetermined number of types in descending order of the difference between the cell defect occurrence rate for the first lot group and the cell defect occurrence rate for the second lot group among a plurality of types. May be.

  Further, for example, the control unit 601 may calculate the cell defect occurrence rate in lot units, wafer units, and chip units of each lot group. Here, a case where calculation is performed in units of lots is taken as an example. For example, the control unit 601 determines, for each of the plurality of types, between each cell failure occurrence rate calculated for each of the first lot groups and each cell failure occurrence rate calculated for each of the second lot groups. An index value iv indicating a significant difference is calculated. The index value iv here may be any of an average difference or a p value. Then, the control unit 601 identifies a type having a significant difference based on the calculated index value iv among a plurality of types. Alternatively, for example, the control unit 601 may specify a predetermined number of types in the order of the calculated index value iv among a plurality of types.

  Here, the cell corresponding to the specified type is an abnormal cell, and the abnormal cell is the plurality of first cells c1n described in FIG. An abnormal cell is a candidate cell for an abnormality. Each of the plurality of first cells c1n is a different type of cell. In addition, a cell that does not correspond to the specified type is a normal cell, and the normal cell is a plurality of second cells c2m described in FIG. Each of the plurality of second cells c2m is a different type of cell.

  Next, processing for creating the above-described <2> exposure simulation table will be described. First, the control unit 601 derives the first feature value W1 based on the exposure simulation of the transfer pattern tp1 of the predetermined layer of each of the plurality of first cells c1n. The transfer pattern tp1 is a transfer pattern when the exposure is performed under a predetermined exposure condition ec using the layout pattern lp1 of the predetermined layer of the plurality of first cells c1n. Then, the control unit 601 derives the second feature value W2 based on the exposure simulation of the transfer pattern tp2 of the predetermined layer of each of the plurality of second cells c2m included in the target circuit and different from the plurality of first cells c1n. The transfer pattern tp2 is a transfer pattern in the case where exposure is performed under a predetermined exposure condition ec using a layout pattern lp2 of a predetermined layer of the plurality of second cells c2m.

  Here, as described above, the first cell c1 is an abnormal cell and the second cell c2 is a normal cell. The predetermined layer is a layer on which exposure is performed, and examples thereof include an Active layer, a Poly layer, and a Metal layer described with reference to FIG. In the present embodiment, all layers to be exposed are subjected to exposure simulation. The predetermined exposure condition ec includes a combination of focus swing and exposure amount swing. Focus swing is adjustment of the height of an exposure lens up and down. For example, conditions such as 0, ± 20, ± 40, and ± 60 [nm] with respect to a specified lens height are used. Can be mentioned. The exposure amount adjustment is adjustment of the light intensity of the light source. For example, conditions such as 0, ± 10 [%] with respect to the specified intensity of the light source can be mentioned. Further, examples of the first feature value W1 and the second feature value W2 include the minimum line width and the minimum wiring extraction width of the transfer pattern tp shown in FIG. The layout pattern lp of the predetermined layer is obtained from the layout pattern lp of each layer of the cell indicated by the cell information.

  In the present embodiment, for example, the control unit 601 performs, for each of the cells included in the target circuit, all the exposure simulations of the combination of the layer and the exposure condition ec, and then performs the abnormal lot group and the normal lot. Divide the simulation results into groups. Here, an exposure simulation is performed for each cell type. If the cell types are the same, the layout pattern lp is the same, so the feature values obtained by the exposure simulation are the same. Therefore, as described above, each of the plurality of first cells c1n is a different type of cell, and each of the plurality of second cells c2m is a different type of cell. Thereby, since various types of feature values can be obtained, the accuracy of failure estimation can be improved.

  8 and 9 are explanatory diagrams showing the output results of the exposure simulation. For example, FIG. 8 shows an exposure simulation result for the cell AAA, and FIG. 9 shows an exposure simulation result for the cell BBB. Although FIGS. 8 and 9 show up to the Metal2 layer, actually, an exposure simulation is performed for all the layers to be exposed. FIGS. 8 and 9 show the exposure simulation results for two types of cells, AAA and BBB. Actually, however, for example, exposure is performed for all types of cells mounted on the target circuit. A simulation is performed.

  FIG. 10 is an explanatory diagram showing an example of a table of exposure simulation results. The control unit 601 tabulates the exposure simulation result. The table 415 includes, for example, fields for cell name, layer, exposure amount, focus, extraction / line width, and simulation value. By setting information in each field, it is stored as a record (for example, 1000-1, 1000-2, etc.).

  In the cell name field, identification information capable of uniquely specifying the cell type is set. As described above, when the cell types are the same, the feature values obtained by the exposure simulation are the same. Taking the record 1000-1 as an example, “AAA” is set in the cell name field. In the layer field, identification information that can uniquely identify the layer is set. Taking the record 1000-1 as an example, “Active” is set in the field of the layer, which indicates the Active layer.

  In the exposure amount field, a value [%] indicating how many percent of the specified intensity is increased or decreased is set. Taking record 1000-1 as an example, “0” is set in the exposure amount field. The field of focus is a value [nm] indicating how much it is moved up and down with respect to the specified height. Taking the record 1000-1 as an example, “0” is set in the focus field.

  In the extraction / line width field, it is set which simulation value of the minimum wiring extraction width or the minimum line width is shown. Taking the record 1000-1 as an example, a “blank width” is set. In the simulation value field, the minimum wiring extraction width or the minimum line width [nm], which is a characteristic value obtained by the exposure simulation, is set. Taking record 1000-1 as an example, “10” is set.

  Next, <3> exposure failure factor analysis will be described. First, the control unit 601 selects one simulation condition from among a plurality of simulation conditions. The simulation condition is a combination of a layer, an exposure condition ec, and a feature value type. The layer is a layer that is exposed as described above, and the exposure condition ec includes focus swing and exposure amount swing as described above. The types of feature values include the minimum wiring extraction width and the minimum line width as described above. The control unit 601 acquires a record 1000 corresponding to the selected simulation condition from the table 415.

  FIG. 11 is an explanatory diagram illustrating an example of a list of simulation results according to simulation conditions. In the example of FIG. 11, the list 612 is obtained from the table 415 by the record 1000 corresponding to the simulation condition = {layer = Metal1, focus = + 20, exposure amount = −10 [%], extraction / line width = extraction width}. Is the result.

  Next, the control unit 601 classifies the list 612 into two groups: a first lot group that is an abnormal lot group and a second lot group that is a normal lot group. A group of the first lot group is referred to as a first group, and a group of the second lot group is referred to as a second group.

  FIG. 12 is an explanatory diagram illustrating a group example. The first group includes the cell BBB, the cell FFF, and the cell JJJ, and the second group includes all the cells other than the cell BBB, the cell FFF, and the cell JJJ.

  Next, the control unit 601 calculates a significant difference between the first feature value W1 derived for each of the plurality of first cells c1n and the second feature value W2 derived for each of the plurality of second cells c2m. The indicated index value iv is calculated. Specifically, for example, the control unit 601 calculates an index value iv indicating a significant difference between the feature value of the abnormal candidate cell group and the feature value of the normal cell group. As index value iv which shows a significant difference, the difference of p value, an average value, etc. are mentioned, for example. The p value is, for example, a probability that there is no difference between the first feature value W1 and the second feature value W2. The difference between the average values is, for example, a difference between the average value of the first feature value W1 and the average value of the second feature value W2.

  For example, when the index value iv is a p value, the control unit 601 determines whether or not there is a significant difference depending on whether or not the index value iv is greater than or equal to the threshold th. For example, when the index value iv is greater than or equal to the threshold th, the control unit 601 determines that there is no significant difference. For example, when the index value iv is less than the threshold th, the control unit 601 determines that there is a significant difference. For example, the threshold th is determined in advance by the user and stored in the storage unit 602. For example, the threshold value th is 0.05.

  For example, when the index value iv is a difference between the average values, for example, the control unit 601 determines whether or not there is a significant difference depending on whether or not the index value iv that is the absolute value of the difference is equal to or greater than the threshold th. judge. For example, when the index value iv is greater than or equal to the threshold th, the control unit 601 determines that there is a significant difference. For example, when the index value iv is less than the threshold th, the control unit 601 determines that there is no significant difference. For example, the threshold th is determined in advance by the user and stored in the storage unit 602.

  For example, the control unit 601 may arrange the combinations of the simulation condition and the index value iv in the order of the index value iv after calculating the index value iv for each of the simulation conditions.

  FIG. 13 is an explanatory diagram showing an example of ranking based on the index value. In the example of FIG. 13, since the index value iv is a p value, the ranking result 613 is arranged in descending order of the index value iv. The ranking result 613 includes fields for ranking, layer, blank / line width, exposure amount, focus, and T test p value. By setting information in each field, it is stored as a record (for example, 1300-1, 1300-2, etc.). Since the fields of layer, extraction / line width, exposure amount, and focus are the same as the fields in the table 415, detailed description thereof is omitted. In the ranking field, a ranking based on the T test p-value is set. The calculated T test p value is set in the T test p value field. For example, the control unit 601 determines whether or not there is a significant difference depending on whether or not the index value iv is greater than or equal to the threshold th in order of higher ranking.

  When it is determined that there is a significant difference, the control unit 601 outputs information indicating that the exposure under the simulation condition is caused by a failure. When it is determined that there is no significant difference, the control unit 601 outputs information indicating that the exposure under the simulation condition is not caused by a failure. Examples of the output format include output to an output device 508 such as a display and output to another device via a network NET.

  For example, when it is determined that there is a significant difference, the control unit 601 newly determines whether or not the index value iv is equal to or greater than the threshold th for the next rank. For example, when it is determined that there is no significant difference, the control unit 601 ends without determining whether or not the index value iv is greater than or equal to the threshold th for the next rank. For example, when the index value iv for the simulation condition with the highest ranking is less than the threshold th, the control unit 601 may output information indicating that there is no suspicion of failure due to exposure because there is no significant difference. .

(Example of analysis support processing procedure by the analysis support apparatus 100)
FIG. 14 is a flowchart illustrating an example of an analysis support processing procedure performed by the analysis support apparatus. First, the analysis support apparatus 100 performs an abnormal cell identification process (step S1401). The analysis support apparatus 100 performs an exposure simulation table creation process (step S1402). The exposure simulation table is, for example, the table 415. Then, after the processes in steps S1401 and S1402, the analysis support apparatus 100 performs an exposure failure factor analysis process (step S1403).

  FIG. 15 is a flowchart showing a detailed description of the abnormal cell specifying process shown in FIG. The analysis support apparatus 100 acquires failure data 411 (step S1501). The analysis support apparatus 100 performs logic failure diagnosis (step S1502). Next, the analysis support apparatus 100 specifies logic failure candidate cells (step S1503).

  The analysis support apparatus 100 identifies a failure candidate cell of the SRAM macro (step S1504). After the processing of step S1503 and step S1504, the analysis support apparatus 100 creates a table 611 that is a list of failure candidate cells (step S1505). The analysis support apparatus 100 distinguishes between the abnormal lot group and the normal lot group (step S1506). The abnormal lot group is the first lot group. The normal lot group is the second lot group. The analysis support apparatus 100 identifies an abnormal cell group (step S1507) and ends a series of processes. As described above, the abnormal cell group is a plurality of first cells c1n.

  FIG. 16 is a flowchart showing a detailed description of the process of creating the exposure simulation table shown in FIG. First, the analysis support apparatus 100 derives each feature value by exposure simulation for each type of cell for each combination of layer and exposure condition ec (step S1601). Then, the analysis support apparatus 100 tabulates the simulation results (step S1602) and ends the series of processes.

  FIG. 17 is a flowchart showing in detail the exposure failure factor analysis process shown in FIG. First, the analysis support apparatus 100 determines whether there is an unselected simulation condition (step S1701). When it is determined that there is an unselected simulation condition (step S1701: Yes), the analysis support apparatus 100 selects one simulation condition from the unselected simulation conditions (step S1702).

  The analysis support apparatus 100 acquires a feature value corresponding to the selected simulation condition from the table 415 (step S1703). The analysis support apparatus 100 calculates an index value iv indicating a significant difference between the first feature value of the abnormal cell group and the second feature value of the normal cell group (step S1704), and the process returns to step S1701. The abnormal cell group is a plurality of first cells c1n, and the normal cell group is a plurality of second cells c2m. When it is determined that there is no unselected simulation condition (step S1701: No), the analysis support apparatus 100 sorts based on the index value iv (step S1705). When the sorting result is the ranking result 613, the analysis support apparatus 100 determines whether there is an unselected simulation condition (step S1706).

  When it is determined that there is an unselected simulation condition (step S1706: Yes), the analysis support apparatus 100 selects a simulation condition with the highest index value iv from the unselected simulation conditions (step S1707). The analysis support apparatus 100 determines whether there is a significant difference (step S1708). If it is determined that there is a significant difference (step S1708: Yes), the analysis support apparatus 100 outputs information indicating that the simulation condition is highly likely to be a failure (step S1709), and returns to step S1706. When it is determined that there is no significant difference (step S1708: No), the analysis support apparatus 100 outputs information indicating that the simulation condition is unlikely to be caused by a failure (step S1710), and ends the series of processes. To do.

  If it is determined that there is no unselected simulation condition (step S1706: No), the analysis support apparatus 100 outputs information indicating that there is a low possibility of a failure due to exposure (step S1711). End the process. Examples of output formats in step S1709, step S1710, and step S1711 include output to other devices via the network NET, output to an output device 508 such as a display, output to a storage unit, and the like.

  As described above, the analysis support apparatus derives the characteristic value of the transfer pattern of the same layer for each of the abnormal cell group and the normal cell group by the exposure simulation under the same exposure condition, and determines the significant value between the characteristic values. Determine the difference. Thereby, it is determined whether or not there is a possibility that a failure has occurred in the combination of the layer and the exposure condition, and the time required for the failure identification is shortened.

  For example, when it is determined that there is no significant difference, it can be determined that no failure has occurred in the combination of the layer and the exposure condition. Therefore, for example, when a failure factor analyst performs a physical analysis in the combination, the failure factor analyst can make a determination such as lowering the priority of the combination. The physical analysis is a physical cross-sectional analysis or a physical separation analysis. In addition, for example, when it is determined that there is a significant difference, it is determined that a failure may have occurred in the combination of the cell, the layer, and the exposure condition, and the failure factor analyst is based on the combination. Physical analysis may be performed. In this way, it is possible to shorten the time required for failure identification. Further, since the exposure simulation is performed in units of cells, the time required for the exposure simulation can be shortened compared with the case where the simulation is performed on the entire target circuit. Further, since the same type of cells may be subjected to a single exposure simulation, the time required for the exposure simulation can be reduced.

  Further, the analysis support apparatus identifies a failure candidate cell corresponding to each position of the failure candidate as an abnormal cell group based on the failure data. Thereby, since the cell with high possibility of abnormality is specified, the accuracy of failure estimation can be improved.

  In addition, the analysis support device is specified based on the number of failure candidate cells corresponding to the cell type for the abnormal wafer and the number of failure candidate cells corresponding to the cell type for the normal wafer among the plurality of types. A cell corresponding to the selected type is defined as an abnormal cell. Thereby, since the kind of cell with high possibility of being abnormal is specified, the accuracy of failure estimation can be improved.

  Each abnormal cell group is a different type of cell. Each normal cell group is a different type of cell. As a result, more feature values can be obtained, so that the accuracy of failure estimation can be improved.

  In addition, the index value indicating the significant difference is, for example, the first feature value of the transfer pattern of each actual predetermined layer of the abnormal cell group based on the derived first feature value and the derived second feature value, and normal This is the probability that there is no difference between the second characteristic value of the transfer pattern of the actual predetermined layer of each cell group. Thereby, a significant difference can be determined and the time required for failure estimation can be shortened.

  The index value indicating a significant difference is, for example, the difference between the average value of the first feature values and the average value of the second feature values. Thereby, a significant difference can be determined and the time required for failure estimation can be shortened.

  The analysis support method described in this embodiment can be realized by executing a prepared analysis support program on a computer such as a personal computer or a workstation. The analysis support program is recorded on a computer-readable recording medium such as a magnetic disk, an optical disk, or a USB (Universal Serial Bus) flash memory, and is executed by being read from the recording medium by the computer. The analysis support program may be distributed through a network such as the Internet.

  The following additional notes are disclosed with respect to the embodiment described above.

(Supplementary note 1)
A first based on an exposure simulation of the transfer pattern of the predetermined layer of each of the plurality of first cells when exposure is performed under a predetermined exposure condition using a layout pattern of the predetermined layer of the plurality of first cells included in the target circuit. Deriving feature values
The predetermined of each of the plurality of second cells when exposed by the predetermined exposure condition using a layout pattern of the predetermined layer of a plurality of second cells included in the target circuit and different from the plurality of first cells. Deriving a second feature value based on exposure simulation of the layer transfer pattern,
Calculating an index value indicating a significant difference between the first feature value derived for each of the plurality of first cells and the second feature value derived for each of the plurality of second cells;
An analysis support method characterized by executing processing.

(Appendix 2) The computer
When a specific signal is input to the input terminal included in the target circuit and a failure occurs in the target circuit, the position of the failure candidate is determined based on the signal output from the output terminal included in the target circuit. Execute the process to identify the cell corresponding to each,
The analysis support method according to appendix 1, wherein the plurality of first cells are the plurality of specified cells.

(Supplementary note 3)
For each of the target circuits included in the first substrate having a plurality of the target circuits, a specific signal is input to an input terminal included in the target circuit and a failure occurs in the target circuit. Identifying a cell corresponding to each of the candidate failure locations based on a signal output from an included output terminal;
For each of the target circuits included in a second substrate that is a second substrate different from the first substrate and has a plurality of target circuits, a specific signal is input to an input terminal included in the target circuit, and the target Identifying a cell corresponding to each of the candidate positions of the failure based on a signal output from an output terminal included in the target circuit when a failure occurs in the circuit;
For each of a plurality of types of cells included in the target circuit, the first number of cells corresponding to the type among the cells specified for the first substrate, and the cells specified for the second substrate A second number of cells corresponding to the type, and
Identifying a plurality of types based on the derived first number and the derived second number from the plurality of types;
Execute the process,
The analysis support method according to appendix 1, wherein the plurality of first cells are cells corresponding to any of the specified types.

(Supplementary note 4) The analysis support method according to any one of supplementary notes 1 to 3, wherein each of the plurality of first cells is a different type of cell.

(Supplementary note 5) The analysis support method according to any one of supplementary notes 1 to 4, wherein each of the plurality of second cells is a different type of cell.

(Additional remark 6) The said index value is based on the said predetermined exposure condition using the layout pattern of the said predetermined layer of these 1st cell based on the derived | led-out 1st feature value and the derived | led-out 2nd feature value. When exposed, the first feature value of the transfer pattern of the predetermined layer of each of the plurality of first cells and the layout pattern of the predetermined layer of the plurality of second cells are exposed according to the predetermined exposure condition. In any case, it is a probability that there is no difference between the second feature value of the transfer pattern of the predetermined layer of each of the plurality of second cells. Analysis support method.

(Supplementary note 7) The analysis support method according to any one of supplementary notes 1 to 5, wherein the index value is a difference between an average value of the first feature values and an average value of the second feature values. .

(Appendix 8)
A first based on an exposure simulation of the transfer pattern of the predetermined layer of each of the plurality of first cells when exposure is performed under a predetermined exposure condition using a layout pattern of the predetermined layer of the plurality of first cells included in the target circuit. Deriving feature values
The predetermined of each of the plurality of second cells when exposed by the predetermined exposure condition using a layout pattern of the predetermined layer of a plurality of second cells included in the target circuit and different from the plurality of first cells. Deriving a second feature value based on exposure simulation of the layer transfer pattern,
Calculating an index value indicating a significant difference between the first feature value derived for each of the plurality of first cells and the second feature value derived for each of the plurality of second cells;
An analysis support program characterized by causing processing to be executed.

(Supplementary Note 9) An exposure simulation of the transfer pattern of the predetermined layer of each of the plurality of first cells in the case where exposure is performed under a predetermined exposure condition using the layout pattern of the predetermined layer of the plurality of first cells included in the target circuit. The first characteristic value based on the first circuit is derived, and the exposure is performed under the predetermined exposure condition using the layout pattern of the predetermined layer of the plurality of second cells included in the target circuit and different from the plurality of first cells. A second feature value is derived based on an exposure simulation of the transfer pattern of the predetermined layer in each of the plurality of second cells, the first feature value derived for each of the plurality of first cells, and the plurality of second cells. An analysis support apparatus, comprising: a control unit that calculates an index value indicating a significant difference between the second feature value derived for each cell.

(Appendix 10)
A first based on an exposure simulation of the transfer pattern of the predetermined layer of each of the plurality of first cells when exposure is performed under a predetermined exposure condition using a layout pattern of the predetermined layer of the plurality of first cells included in the target circuit. Deriving feature values
The predetermined of each of the plurality of second cells when exposed by the predetermined exposure condition using a layout pattern of the predetermined layer of a plurality of second cells included in the target circuit and different from the plurality of first cells. Deriving a second feature value based on exposure simulation of the layer transfer pattern,
Calculating an index value indicating a significant difference between the first feature value derived for each of the plurality of first cells and the second feature value derived for each of the plurality of second cells;
A recording medium on which an analysis support program for executing processing is recorded.

100 Analysis support device 411 Failure data 414 Test pattern W1 First feature value W2 Second feature value iv Index value lp Layout pattern tp Transfer pattern c1 First cell c2 Second cell ec Exposure condition

Claims (6)

Computer
A first based on an exposure simulation of the transfer pattern of the predetermined layer of each of the plurality of first cells when exposure is performed under a predetermined exposure condition using a layout pattern of the predetermined layer of the plurality of first cells included in the target circuit. Deriving feature values
The predetermined of each of the plurality of second cells when exposed by the predetermined exposure condition using a layout pattern of the predetermined layer of a plurality of second cells included in the target circuit and different from the plurality of first cells. Deriving a second feature value based on exposure simulation of the layer transfer pattern,
Calculating an index value indicating a significant difference between the first feature value derived for each of the plurality of first cells and the second feature value derived for each of the plurality of second cells;
An analysis support method characterized by executing processing. The computer is
When a specific signal is input to the input terminal included in the target circuit and a failure occurs in the target circuit, the position of the failure candidate is determined based on the signal output from the output terminal included in the target circuit. Execute the process to identify the cell corresponding to each,
The analysis support method according to claim 1, wherein the plurality of first cells are the plurality of specified cells. The computer is
For each of the target circuits included in the first substrate having a plurality of the target circuits, a specific signal is input to an input terminal included in the target circuit and a failure occurs in the target circuit. Identifying a cell corresponding to each of the candidate failure locations based on a signal output from an included output terminal;
For each of the target circuits included in a second substrate that is a second substrate different from the first substrate and has a plurality of target circuits, a specific signal is input to an input terminal included in the target circuit, and the target Identifying a cell corresponding to each of the candidate positions of the failure based on a signal output from an output terminal included in the target circuit when a failure occurs in the circuit;
For each of a plurality of types of cells included in the target circuit, the first number of cells corresponding to the type among the cells specified for the first substrate, and the cells specified for the second substrate A second number of cells corresponding to the type, and
Identifying a plurality of types based on the derived first number and the derived second number from the plurality of types;
Execute the process,
The analysis support method according to claim 1, wherein the plurality of first cells are cells corresponding to any of the specified types.   The analysis support method according to claim 1, wherein each of the plurality of first cells is a different type of cell.   The analysis support method according to any one of claims 1 to 4, wherein each of the plurality of second cells is a different type of cell. On the computer,
A first based on an exposure simulation of the transfer pattern of the predetermined layer of each of the plurality of first cells when exposure is performed under a predetermined exposure condition using a layout pattern of the predetermined layer of the plurality of first cells included in the target circuit. Deriving feature values
The predetermined of each of the plurality of second cells when exposed by the predetermined exposure condition using a layout pattern of the predetermined layer of a plurality of second cells included in the target circuit and different from the plurality of first cells. Deriving a second feature value based on exposure simulation of the layer transfer pattern,
Calculating an index value indicating a significant difference between the first feature value derived for each of the plurality of first cells and the second feature value derived for each of the plurality of second cells;
An analysis support program characterized by causing processing to be executed.

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