A device selection method and system for chip analysis

Abstract

The application provides a device selection method for chip analysis, and specifically comprises the following steps: step S1: selecting a plurality of chip subareas divided in equal areas as target subareas, which are uniformly distributed on the chip; step S2: acquiring attribute information of devices in each target subarea respectively, determining a plurality of attributes from the attribute information of the devices, and performing aggregation on the devices in each target subarea based on the plurality of attributes to obtain a plurality of attribute combination lists; step S3: selecting a plurality of attributes in the common attributes in the attribute combination lists as a public attribute combination; and step S4: selecting devices in the target subarea that meet the public attribute combination as target devices for intra-chip consistency analysis. Compared with the prior art, the application performs intra-chip consistency analysis, so that the test of product chip key devices in a real physical environment can better reflect the product chip state, and more valuable information is provided for the improvement of the yield of chip products.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of semiconductor design and production technology, and in particular to a device selection method and system for chip analysis. BACKGROUND

[0002] In the whole life cycle of advanced process, there is an important risk affecting product yield: the main purpose of process development stage (relatively simple environment) is to develop platform process for a certain process node, while product introduction and mass production stage (complex and changeable environment) is oriented to actual product results. Chip products are diversified, and the design maturity and process sensitivity of each chip are different, and even there may be great differences, so in the process of chip introduction, there will be many problems that are not found or not paid attention to in the process development stage, resulting in the disconnection between process development and product introduction.

[0003] Under the traditional test chip process, customers can only infer the state of the corresponding device in the product chip by testing the test structure in the test chip. However, with the continuous evolution of process nodes, the differences between the physical environment faced by the devices in the product chip and the test structures in the test chip gradually manifest. Therefore, how to use the real product chip to design the built-in test chip to realize the test of the key devices in the product chip in the real physical environment has great significance for the improvement of chip product yield. In the design of the built-in test chip, how to select the devices for testing according to the huge device information is a crucial step. SUMMARY

[0004] The main purpose of the present application is to overcome the shortcomings in the prior art, and provide a device screening method and system for chip analysis, which can be used for on-chip consistency analysis. On-chip consistency analysis considers the differences caused by the distribution of devices with the same device properties in different location areas of the chip, so it is necessary to provide devices for on-chip consistency analysis when selecting test devices, and therefore the present application provides a reasonable and effective device screening method for on-chip consistency analysis.

[0005] To achieve the above object, the application provides a device selection method for chip analysis, which specifically comprises the following steps: step S1: selecting several chip subareas divided equally in area as target subareas, which are evenly distributed on the chip; step S2: obtaining attribute information of devices in each target subarea respectively, determining several attributes from the attribute information of the devices, and aggregating the devices in each target subarea based on the several attributes to obtain several attribute combination lists; each attribute combination list corresponds to a target subarea; step S3: selecting several attributes in the common attributes in the attribute combination lists as common attribute combinations; step S4: selecting devices in the target subareas satisfying the common attribute combinations as target devices for on-chip consistency analysis.

[0006] As a further improvement of the application, in step S1, the chip is divided into NXM chip subareas equally in area, wherein N and M are positive integers.

[0007] As a further improvement of the application, in step S2, the following substep S21 is included: S21: selecting one or more target core areas in each target subarea.

[0008] As a further improvement of the application, in step S2, the attribute information of the devices in each target subarea is obtained specifically by obtaining the attribute information of the devices in the one or more target core areas in the target subarea as the attribute information of the devices in the target subarea.

[0009] As a further improvement of the application, the target core area is determined in the following manner: selecting an area with a preset size as the target core area in the center of the target subarea.

[0010] As a further improvement of the application, the attribute combination list comprises combinations between different field values of the several attributes.

[0011] As a further improvement of the application, in step S4, the devices satisfying the common attribute combinations are selected as target devices in the target core areas of the target subareas for on-chip consistency analysis.

[0012] As a further improvement of the present application, in step S4, the devices in the target partition that meet the public attribute combination are selected as target devices, and a device screening method based on the public attribute combination is used: step S41: obtain a regional device list through data aggregation, the regional device list contains information: target core regions in several target partitions, several public attributes, and the number of devices in the target core regions in the target partition that meet the public attributes; obtain a device screening index that meets the public attribute combination; step S42: based on the screening condition, the target devices are screened according to the regional device list; wherein the screening condition includes: condition one. The number of target devices in the target core region in the target partition does not exceed a preset number; condition two. The difference between the number of target devices that meet the device attribute and the device screening index is the smallest.

[0013] In the regional device list, a device is located only in a target core region in one of the target partitions, and the device meets several public attributes.

[0014] As a further improvement of the present application, in step S41, the regional device list is obtained through data aggregation, including: obtaining device data containing information: public attribute combination of the device and target core region in the target partition to which the device belongs; aggregating the device data, the aggregation condition is the public attribute of the device and the target core region in the target partition to which the device belongs, and the aggregation result is the number of devices; each aggregation result data is used to represent the number of devices that meet a certain public attribute in a target core region in a certain target partition; after the aggregation of the device data is completed, the obtained aggregation result data is the regional device list.

[0015] As a further improvement of the present application, in step S42, the demand sample size and the candidate size corresponding to the public attribute combination are set for the regional device list, and the values of the demand sample size and the candidate size corresponding to the public attribute combination are updated in real time during the screening, and the value of the candidate size corresponding to each public attribute is updated;

[0016] The value of the demand sample size corresponding to the public attribute combination is the current to-be-screened device index that meets the public attribute combination; the value of the candidate size corresponding to the public attribute combination is the number of devices in the target core region in the target partition that meet the public attribute combination; the value of the demand sample size corresponding to the public attribute is the current to-be-screened device index that meets the public attribute;

[0017] The screening condition further includes: first, screen the devices that meet the public attribute whose difference between the number of devices in the target core region in the target partition and the to-be-screened device index is small.

[0018] As a further improvement of the present application, in the step S42, the screening condition further comprises: when screening the target device satisfying the combination of the common attributes, selecting the device in the target core area in a specific target partition for screening; the target core area in the specific target partition refers to that the target core area in the target partition satisfies less categories of other common attributes than the target core area in other target partitions in the target partition.

[0019] In the step S41, the data aggregation method comprises: defining each row of the to-be-aggregated data as a piece of data, and each column as a field, obtaining the to-be-aggregated data for aggregation, and triggering batch processing of the to-be-aggregated data based on a plurality of fields according to the memory usage, dividing the to-be-aggregated data into a plurality of batches, and aggregating the to-be-aggregated data in batches in the memory; moving the aggregation result data of the completed batch from the memory into an aggregation result file, continuing to aggregate the data of the next batch in the memory, until the aggregation of all the to-be-aggregated data is completed, and finally obtaining an aggregation result file; wherein the aggregation result data comprises an aggregation condition and a count field, and the count field is used to represent the number of data satisfying the aggregation condition; the to-be-aggregated data is stored in a column-oriented manner; and the to-be-aggregated data is aggregated by reading the fields at the corresponding positions by column to obtain a piece of data. Both the column-oriented storage data and the batch reading and filtering can effectively solve the problem of insufficient memory when aggregating large amounts of data.

[0020] To further achieve the above object, the present application further provides a device selection system for chip analysis, comprising a storage device, wherein the storage device stores a plurality of instructions, and the instructions are adapted to be loaded and executed by a processor to implement the device selection method for chip analysis.

[0021] The device selection method for chip analysis of the present application has the following advantages: by combining and counting the device attributes of each chip partition, the combination of common attributes shared by each chip partition is screened out, so as to screen the devices satisfying the combination of common attributes in each chip partition, and to perform intra-chip consistency analysis, so that the testing of the key devices of the product chip in the real physical environment can better reflect the state of the product chip, and provide more valuable information for improving the yield of the chip product. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 is a step flow chart of the device selection method for chip analysis according to the preferred embodiment of the present application.

[0023] Figure 2 is a schematic diagram of a chip partition. Detailed Implementation

[0024] The following embodiments are intended to enable those skilled in the art to more fully understand the present invention, but do not limit the invention in any way.

[0025] The embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0027] The terminology used in this invention is explained below:

[0028] Chip Region: Each chip region is a two-dimensional planar rectangle, defined by the coordinates of its four vertices. Chip regions do not overlap. For example, Table 1 shows an example of chip regions, including nine chip regions from r1 to r9, where (X1, Y1) and (X2, Y2) are the coordinates of the two points on the diagonal of the two-dimensional planar rectangle corresponding to the chip region.

[0029] Table 1. Coordinates of the two diagonal points of the two-dimensional rectangular plane containing the chip area.

[0030]

[0031]

[0032] Device attributes: Multiple device attributes may belong to the same device, and each device attribute has a sample quantity requirement. Table 2 shows an example of device attributes. Each row represents one device information, where Length, Width, SDB (single diffusion break), model, and location point coordinates (X,Y) can all be used as device attributes.

[0033] Table 2. Device Properties

[0034] Length Width SDB model X Y 0.010 0.060 0 Nch08 1.1 1.1 0.010 0.070 1 Nch08 1.2 1.2 0.010 0.080 2 Nch08 1.3 1.3 0.010 0.080 2 Nch08 4.1 1.1 0.016 0.080 1 Nch08 4.2 1.2 0.010 0.060 2 Nch08 7.1 1.1 0.010 0.080 2 Nch08 7.2 1.2 0.010 0.080 2 Nch08 7.3 1.3 0.016 0.080 2 Nch08 1.1 4.1 0.010 0.080 2 Nch08 4.1 4.1 0.016 0.080 2 Nch08 4.2 4.2 0.016 0.080 2 Nch08 4.3 4.3 0.010 0.070 1 Nch08 7.1 4.1 0.016 0.080 2 Nch08 1.1 7.1 0.010 0.080 2 Nch08 1.2 7.2 0.010 0.060 0 Nch08 1.3 7.3 0.010 0.080 2 Nch08 4.1 7.1 0.010 0.070 0 Nch08 7.1 7.1 0.016 0.080 0 Nch08 7.2 7.2 0.010 0.080 2 Nch08 7.3 7.3

[0035] The following embodiments are intended to enable those skilled in the art to more fully understand the present invention, but do not limit the invention in any way.

[0036] A device selection method for chip analysis specifically includes the following steps:

[0037] Step S1: As shown in the figure, the chip is divided into N x M equal-area chip partitions, in this embodiment N = 3, M = 3, that is, the entire chip is divided into 3 x 3 equal-area chip partitions. 5 target partitions are selected at equal intervals, that is, P1, P2, P3, P4, P5 in Figure 1 Figure 1

[0038] Step S2: S21: Select a target core region in each target partition respectively; in this embodiment, the target core region is determined by determining an equal-area target core region at the center of the target partition, and the relative position relationship between the target partition and the target core region can be referred to in Figure 1

[0039] Step S3: Select a (or several) aggregated attribute combination that exists in all 5 partitions, which is the common attribute combination marked in Table 3. As can be seen, there is only one common attribute combination as shown in Table 4 in this embodiment, and there may be several in other embodiments.

[0040] Table 3. Common attribute combination

[0041]

[0042] Table 4. Common attribute combination

[0043] Length Width SDB model 0.010 0.080 2 Nch08

[0044] ​​​Step S4: select one (or several) public attribute combination, take the attribute combination described therein as the selection condition of the device, and perform (or several times) device selection in the target core area of each target partition as the target device selection for intra-chip consistency analysis. In this embodiment, the device screening method based on the public attribute combination is used to achieve:

[0045] Step S41: obtain a region device list through data aggregation, the region device list containing information: the target core area in the target partition, the public attribute, and the number of devices in the target core area in the target partition that meet the public attribute; and obtain a device screening index that meets the public attribute combination;

[0046] Step S42: screen out the target device according to the region device list based on the screening condition;

[0047] The screening condition includes: condition one. The number of target devices in the target core area in the target partition does not exceed a preset number; and condition two. The difference between the number of target devices that meet the public attribute combination and the device screening index is minimum.

[0048] In the region device list, one device is only located in the target core area in one target partition, and the device meets the public attribute.

[0049] In step S41, the region device list is obtained through data aggregation, including: obtaining device data containing information: the public attribute combination of the device and the target core area in the target partition to which the device belongs; and aggregating the device data, the aggregation condition being the public attribute of the device and the target core area in the target partition to which the device belongs, and the aggregation result being the number of devices; each aggregation result data is used to represent the number of devices that meet a certain public attribute in a target core area in a certain target partition; after the aggregation of the device data is completed, the obtained aggregation result data is the region device list.

[0050] In step S42, the demand sample size and the candidate size corresponding to the public attribute combination are set for the region device list, and the values of the demand sample size and the candidate size corresponding to the public attribute combination are updated in real time during the screening, and the value of the candidate size corresponding to each public attribute is also updated;

[0051] The value of the demand sample size corresponding to the public attribute combination is the current screening device index that meets the public attribute combination; the value of the candidate size corresponding to the public attribute combination is the number of devices in the target core area in the target partition that meet the public attribute combination; and the value of the demand sample size corresponding to the public attribute is the current screening device index that meets the public attribute;

[0052] The screening condition further comprises: screening the devices in the target core region in the target subregion that satisfy the common attribute with a small difference between the number of the devices and the device index to be screened.

[0053] In step S42, the screening condition further comprises: when screening the target devices satisfying the combination of the common attributes, selecting the devices in the target core region in a specific target subregion for screening; the target core region in the specific target subregion refers to the target core region in the target subregion, and the devices in the target core region satisfy fewer categories of other common attributes than the devices in the other target core regions in the other target subregions.

[0054] In order to facilitate understanding of the data aggregation method of the present application, the method of data aggregation is exemplified by the present embodiment. In the data aggregation method of the present embodiment, each row of the to-be-aggregated data is defined as a piece of data, and each column is a field. The to-be-aggregated data is obtained for aggregation, and the to-be-aggregated data is divided into batches based on a number of fields according to the memory usage, and the to-be-aggregated data is divided into a number of batches. The to-be-aggregated data is aggregated in batches in the memory. The aggregated result data of the completed batch is moved from the memory to the aggregated result file, and the aggregation of the next batch of data is continued in the memory until the aggregation of all the to-be-aggregated data is completed, and the final aggregated result file is obtained. The aggregated result data includes an aggregation condition and a count field, and the count field is used to represent the number of data pieces satisfying the aggregation condition.

[0055] In some specific embodiments, the to-be-aggregated data is stored in a column-oriented manner; the to-be-aggregated data is obtained for aggregation by reading the fields in the corresponding position by column to obtain a piece of data for aggregation.

[0056] The aggregation condition of the example comprises a number of fields for aggregation, including a number of original columns or / and a number of derived columns; the original column is a field in a column of the to-be-aggregated data, and the derived column is a field generated by logically combining a number of fields in a number of columns of the to-be-aggregated data.

[0057] In some specific examples, a preset memory is used to store an upper limit of the number of rows of aggregated result data. The data aggregation method of the example further includes: judging whether the currently stored aggregated result data in the memory reaches the upper limit, and if so and there is un-aggregated data, triggering batch processing of the data to be aggregated. In the data aggregation method of the example, the aggregation and real-time judgment of whether batch processing is needed are performed simultaneously. In batch processing, the aggregated result data stored in the memory that no longer belongs to the current batch of aggregation is temporarily stored in a temporary file; and after the aggregation of the current batch is completed, the aggregated result data in the temporary file is moved into the memory. Batch processing is performed on all data, and even if some data has been aggregated after batch processing, it will also be moved to the temporary file, and will be stored in the memory and then formally moved to the aggregated result data until the batch data is processed.

[0058] The calculation method of the upper limit of the example is as follows: the number of bytes of the currently available memory is obtained, denoted as Mem; the number of bytes occupied by each piece of data in the data to be aggregated is obtained, denoted as Agg. In the example, the number of bytes occupied by each piece of data is the same. The maximum number of rows M is calculated by the formula M = [Mem x a / Agg], where the symbol [] represents taking the integer of the calculation result; a is a preset value of the proportion of the available memory of the processor. In a better example, a = 80%.

[0059] In some embodiments, let the total number of rows of data to be aggregated be N, and the maximum number of rows of memory used to store the aggregated result data be M; the specific execution process of the data aggregation method is as follows: step 1): initialize i = 1, j = 0; wherein, i ∈ [1, N], j ∈ [0, M]; set filter used to represent filtering conditions, and the number of filtering conditions m in filter; initialize filter as empty (filter as empty means that any data meets the filtering condition), m = 0; create a temporary file; set the aggregation starting position set A; step 2): read the i-th field value in each column data of the data to be aggregated as the aggregation condition to form a piece of data and record it as R; judge whether R meets filter: if yes, go to step 3); if no, go to step 5); step 3): judge whether the memory has stored the aggregation result data with the same aggregation condition as R: if yes, add 1 to the count field of the aggregation result data, and go to step 5); if no, add a piece of aggregation result data to the memory, the aggregation condition of the aggregation result data is the aggregation condition of R, and the count field of the aggregation result data is 1; let j = j + 1; step 4): judge whether j is equal to M: if no, go directly to step 5); if yes, let m = m + 1, obtain all field values of the m-th column field, and divide all data to be aggregated into several batches by using these field values, determine one of the batches as the current aggregation batch, and add the field values used to filter the current batch to filter as the latest filtering condition (obtain all field values of the column, and perform binary batch or even multi-binary batch, rather than using a certain field value to perform binary batch, so the position may be backtracked several times for aggregation processing of different batches); move the aggregation result data in the memory that does not meet filter to the temporary file, and let a m = i (the subscript m relates to the current value of m, for example, the first batch, here a1 = i, which is used to record the position of the i-th row of data), and record the aggregation state of the batch corresponding to the element, and let a mStep 5): judging whether i is equal to N: if not, setting i = i + 1 to step 2); if yes, writing all the aggregation result data in the memory into the aggregation result file, deleting the latest filter condition in the filter, setting m = m - 1 to step 6); Step 6): judging whether the aggregation start position set A has no element: if yes, completing the aggregation of the data to be aggregated to obtain the aggregation result file; if not, obtaining the latest element in the aggregation start position set A, judging whether the element has corresponding un-aggregated batches: if yes, setting the element to i, determining a current batch in the un-aggregated batches corresponding to the element, adding the field value used for screening the current batch as the latest filter condition to the filter, setting m = m + 1, moving the aggregation result data belonging to the current batch in the temporary file into the memory, changing the value of j to the number of aggregation result data in the memory to step 2); if not, going to step 7); Step 7): deleting the latest element in the aggregation start position set A to step 6).

[0060] The data aggregation system provided in the embodiment includes a storage device, and the storage device stores a plurality of instructions, which are loaded by a processor and executed to implement the data aggregation method of the embodiment. The example data to be aggregated, the aggregation result file and the temporary file can be stored in different storage devices or in one storage device, which is not limited. The storage device can be a database, a disk, a hard disk, etc. The example database storage can be cloud storage or distributed storage, which is not limited.

[0061] The following specific examples can make the skilled in the art more fully understand the specific aggregation process, but do not limit the present application in any way. It should be noted that, for ease of description, the parameters used in this embodiment are small. The example parameters are not limited to the actual situation.

[0062] The following is an example of N = 28 data to be aggregated. For ease of description, as shown in Table 5 below, this column can be stored in an actual database or not. Set M = 6, i.e., the maximum number of rows of the memory for storing aggregation result data is 6. The aggregation Length (length), Width (width), and Model (device model) columns, and the sa in the table refer to the distance from stress (stress) to the side of transistor a.

[0063] Table 5. Data to be aggregated

[0064] Row number Length Width sa Model 1 0.010 0.060 0.04 Nch08 2 0.010 0.060 0.04 Nch08 3 0.010 0.060 0.04 Nch08 4 0.010 0.080 0.04 Nch08 5 0.010 0.080 0.04 Nch08 6 0.016 0.060 0.04 Nch08 7 0.016 0.060 0.04 Nch08 8 0.016 0.060 0.04 Nch08 9 0.016 0.060 0.04 Nch08 10 0.016 0.060 0.04 Nch08 11 0.016 0.060 0.04 Nch08 12 0.016 0.060 0.04 Nch08 13 0.016 0.060 0.04 Nch08 14 0.016 0.080 0.04 Nch08 15 0.010 0.060 0.04 Pch08 16 0.010 0.060 0.04 Pch08 17 0.010 0.060 0.04 Pch08 18 0.010 0.080 0.04 Pch08 19 0.010 0.080 0.04 Pch08 20 0.016 0.060 0.04 Pch08 21 0.016 0.060 0.04 Pch08 22 0.016 0.060 0.04 Pch08 23 0.016 0.060 0.04 Pch08 24 0.016 0.060 0.04 Pch08 25 0.016 0.060 0.04 Pch08 26 0.016 0.060 0.04 Pch08 27 0.016 0.060 0.04 Pch08 28 0.016 0.080 0.04 Pch08

[0065] The field values of the three columns of Length, Width and Model are read column by column to obtain one piece of data for aggregation, at this time the column sa is ignored and only the three columns are considered; until i=20, at this time the aggregated result data shown in Table 6 is stored in the memory, which has reached the maximum number of rows of the memory, and there is no aggregated result data in the memory with the same aggregation condition as the i=20th row of data.

[0066] Table 6. Aggregated result data

[0067]

[0068] In the middle

[0069] At this time, j=M is satisfied, Length=0.010 is added to the filter condition filter, and the two rows of Length=0.016 in the memory are written to the temporary file, at this time the memory data is shown in Table 7.

[0070] Table 7. Memory data

[0071] Length Width Model Count 0.010 0.060 Nch08 3 0.010 0.080 Nch08 2 0.016 0.060 Nch08 0 0.016 0.080 Nch08 0 0.010 0.060 Pch08 3 0.010 0.080 Pch08 2

[0072] At this time, the parameter i=20, and there is only one condition Length=0.010 in the filter. Since the Length of i=20 to 28 is all 0.016, aggregation can be performed, that is, after the aggregation of i=28 is completed, all data satisfying the filter condition has been aggregated, so it can be directly written to the file F, that is, the file F shown in Table 8 is obtained, and the memory data becomes as shown in Table 9.

[0073] Table 8. File F

[0074] Length Width Model Count 0.010 0.060 Nch08 3 0.010 0.080 Nch08 2 0.010 0.060 Pch08 3 0.010 0.080 Pch08 2

[0075] Table 9. Memory data

[0076] Length Width Model Count 0.010 0.060 Nch08 0 0.010 0.080 Nch08 0 0.016 0.060 Nch08 0 0.016 0.080 Nch08 0 0.010 0.060 Pch08 0 0.010 0.080 Pch08 0

[0077] After that, the two rows in the temporary file are moved into the memory, and the memory data is shown in Table 10.

[0078] Table 10. Memory data

[0079]

[0080]

[0081] At this time, the filter is actually equivalent to Length not equal to 0.010, and returns to i=20, and the aggregation is continuously performed until i=28 to complete all data aggregation, and the aggregated result data in the memory is written into a file F to obtain a final aggregation result file as shown in Table 11, and the aggregation of the data to be aggregated as shown in Table 10 is completed.

[0082] Table 11. Aggregation result file

[0083] Length Width Model Count 0.010 0.060 Nch08 3 0.010 0.080 Nch08 2 0.010 0.060 Pch08 3 0.010 0.080 Pch08 2 0.016 0.060 Nch08 8 0.016 0.080 Nch08 1 0.016 0.060 Pch08 8 0.016 0.080 Pch08 1

[0084] In summary, the application aggregates and counts the device attributes of each chip partition, filters out a common attribute combination shared by each chip partition, and selects devices in each chip partition that meet the common attribute combination for intra-chip consistency analysis, so that the testing of these product chip key devices in a real physical environment can better reflect the product chip state, and more valuable information is provided for the improvement of chip product yield.

[0085] The above only provides preferred embodiments of the present application and is not intended to limit the present application. Various modifications and changes can be made by those skilled in the art based on the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A device selection method for chip analysis, characterized by, Specifically comprising the following steps: Step S1: selecting several chip partitions divided by equal area as target partitions, the target partitions are uniformly distributed on the chip; Step S2: Selecting one or more target core regions in each of the target partitions respectively; Respectively acquiring attribute information of devices in each of the target partitions, determining several attributes from the attribute information of the devices, and respectively aggregating the devices in each of the target partitions based on the several attributes to obtain several attribute combination lists; each of the attribute combination lists corresponds to a target partition; Step S3: selecting multiple attributes in the common attributes in the attribute combination list as a public attribute combination; Step S4: selecting devices in the target partitions that meet the public attribute combination as target devices for intra-chip consistency analysis, specifically comprising the following steps: Step S41: obtaining a regional device list through data aggregation, the regional device list contains information: target core regions in several target partitions, several public attributes, and the number of devices in the target core regions in the target partitions that meet the public attributes; and obtaining a device screening index that meets the public attribute combination; Obtaining the regional device list through data aggregation includes: obtaining device data containing information: public attribute combinations of devices and target core regions in target partitions to which the devices belong; and aggregating the device data, the aggregation condition being the public attributes of the devices and the target core regions in the target partitions to which the devices belong, and the aggregation result being the number of devices; each of the aggregation result data is used to represent the number of devices that meet a certain public attribute in a target core region in a certain target partition; after the aggregation of the device data is completed, the obtained aggregation result data is the regional device list; Step S42: screening target devices from the regional device list based on a screening condition; The screening condition includes: condition one. The number of target devices in the target core regions in the target partitions does not exceed a preset number; and condition two. The number of target devices that meet the public attribute combination is minimum different from the device screening index.

2. The device selection method of claim 1, wherein: In the step S1, the chip is divided into NXM chip partitions by equal area, wherein N and M are positive integers.

3. The device selection method of claim 1, wherein, In the step S2, the attribute information of the devices in each of the target partitions is acquired, specifically comprising: Acquiring attribute information of devices in the one or more target core regions in the target partitions as the attribute information of the devices in the target partitions.

4. The device selection method of claim 1, wherein The determination method of the target core region includes: selecting a region with a preset area size as a target core region in the center of the target partition.

5. The device selection method of claim 1, wherein: The attribute combination list includes combinations between different field values of the several attributes.

6. The device selection method of claim 1, wherein, In the step S4, in the target core region of the target partition, the devices that meet the public attribute combination are selected as target devices for intra-chip consistency analysis.

7. A device selection system for chip analysis, characterized by, A storage device having stored therein a plurality of instructions adapted to be loaded by a processor and to perform the device selection method for chip analysis according to any one of claims 1 to 6.

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