Semiconductor machine output data processing method and processing device

Abstract

The application relates to a semiconductor machine output data processing method and device. The method comprises the following steps: acquiring output data of a semiconductor machine; loading the output data into a first array of a target dimension; classifying and analyzing a file header corresponding to the output data to obtain element information, and storing the element information into a second array; and merging the first array and the second array to obtain target data. The method can improve processing efficiency and accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of semiconductor, in particular to a semiconductor machine output data processing method and processing device. BACKGROUND

[0002] The measurement point information generated by a WAT (Wafer Acceptance Test) machine is usually output in the form of raw data, which needs to be further sorted and extracted before being used to generate IV-curve (current-voltage curve) and then obtain the corresponding test results.

[0003] In the traditional technology, engineers mainly rely on Excel tables to gradually sort out the measurement point information generated by the WAT machine. However, this method needs manual operation, takes a long time, and is prone to errors due to human negligence. SUMMARY

[0004] Therefore, it is necessary to provide a semiconductor machine output data processing method and processing device capable of improving processing efficiency and accuracy in view of the above technical problems.

[0005] In a first aspect, the present application provides a semiconductor machine output data processing method, comprising:

[0006] obtaining output data of a semiconductor machine;

[0007] loading the output data into a first array of a target dimension;

[0008] classifying and analyzing the file header corresponding to the output data to obtain element information, and storing the element information into a second array;

[0009] merging the first array and the second array to obtain target data.

[0010] In one embodiment, the loading of the output data into the first array of the target dimension comprises:

[0011] extracting each file header of the output data, and counting different value combinations corresponding to the first element information group in the file header to obtain each target combination;

[0012] dividing the output data into different first data groups based on the value of the second element information group in the output data, at least one element information in the first element information group and the second element information group being different;

[0013] obtaining a preset data feature of the output data, and constructing a first array based on the preset data feature;

[0014] Process the first data packet based on the target combination, and store the processed first data packet into the first array.

[0015] In one embodiment, the preset data feature of the output data is acquired, and a first array is constructed based on the preset data feature, including:

[0016] Based on the number of conditions corresponding to the output data and the number of rows of output data corresponding to each condition, the number of rows of the first array is determined;

[0017] Based on the number of values corresponding to the variable element information corresponding to the output data, the number of columns of the first array is determined, and the target dimension includes the number of rows and the number of columns;

[0018] The first array is constructed based on the number of rows and the number of columns.

[0019] In one embodiment, the first data packet is processed based on the target combination, and the processed first data packet is stored into the first array, including:

[0020] The first data packet is cropped based on the target combination to obtain a second data packet;

[0021] The value of the target combination corresponding to the second data packet and the second data packet are respectively stored into the first array.

[0022] In one embodiment, the element information is obtained by classifying and analyzing the file header in the output data, including:

[0023] The constituting rule of a preset file header is acquired;

[0024] The element information is obtained by classifying and analyzing the file header corresponding to the output data based on the constituting rule of the preset file header.

[0025] In one embodiment, the first array and the second array are merged to obtain target data, including:

[0026] The value of the target combination corresponding to each second data packet is extracted from the first array;

[0027] Based on the value of the target combination corresponding to each second data packet and the element information in the second array, the header information of the target data is obtained;

[0028] The corresponding second data packet is extracted from the first array as the content of the target data;

[0029] The target data is obtained based on the header information and the content.

[0030] In one embodiment, the method further comprises, before extracting the file header in the output data:

[0031] The output data is classified based on the working voltage of the semiconductor device.

[0032] In one embodiment, the method further comprises:

[0033] Obtaining a data template and test data corresponding to the data template, the data template being used for the semiconductor machine to output the output data;

[0034] In the case that the test data is successfully stored in the corresponding first array and second array, outputting a first prompt information that the data template is correct;

[0035] In the case that the test data is not successfully stored in the corresponding first array and second array, outputting a second prompt information that the data template has a problem, the second prompt information being used for correcting the data template;

[0036] The output data of the semiconductor machine is obtained, comprising:

[0037] The output data of the semiconductor machine based on the correct data template is obtained.

[0038] In one embodiment, the method further comprises:

[0039] The target data is processed by target software to obtain a target curve array.

[0040] In a second aspect, the application further provides a semiconductor machine output data processing device, the device comprising:

[0041] An output data obtaining module, configured to obtain output data of a semiconductor machine;

[0042] A first array generating module, configured to load the output data into a first array of a target dimension;

[0043] A second array generating module, configured to classify and analyze a file header corresponding to the output data to obtain element information, and store the element information into a second array;

[0044] A merging module, configured to merge the first array and the second array to obtain target data.

[0045] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in any one of the above embodiments when executing the computer program.

[0046] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method in any one of the above embodiments when executed by a processor.

[0047] In a fifth aspect, the present application provides a computer program product, comprising a computer program, and the computer program implements the steps of the method in any one of the above embodiments when executed by a processor.

[0048] The semiconductor machine output data processing method and the processing device, after obtaining the output data of the semiconductor machine, load the output data into a first array of a target dimension; classify and analyze a file header corresponding to the output data to obtain element information, and store the element information into a second array; and merge the first array and the second array to obtain target data. All the processes are automatically processed by the computer to obtain the target data, thereby improving the processing efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0050] Figure 1 An application environment diagram of the semiconductor machine output data processing method in one embodiment;

[0051] Figure 2 A flowchart of the semiconductor machine output data processing method in one embodiment;

[0052] Figure 3 A flowchart of the data filling step in one embodiment;

[0053] Figure 4 A schematic diagram of the first array corresponding to C1=2, D1=2, L1=2 in one embodiment;

[0054] Figure 5 A schematic diagram of the merging process of the first array and the second array in one embodiment;

[0055] Figure 6A schematic diagram of a target curve array in one embodiment;

[0056] Figure 7 A flowchart of a semiconductor machine output data processing method in another embodiment;

[0057] Figure 8 A block diagram of a semiconductor machine output data processing device in one embodiment;

[0058] Figure 9 An internal structure diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0060] The semiconductor machine output data processing method provided by the embodiments of the present application can be applied in an application environment as shown in the figure. Figure 1 The terminal 102 communicates with the semiconductor machine 104 through a network. The terminal 102 can be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television, a smart air conditioner, a smart vehicle device, a projection device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc.

[0061] The semiconductor machine 104 includes a WAT (Wafer Acceptance Test) machine.

[0062] The output data includes measurement point information of each semiconductor element measured by the WAT machine, and the measurement point information is used to generate an IV curve of the semiconductor element.

[0063] The semiconductor device includes various types of transistors, etc., and at least one of the key information features of each semiconductor device is different. The key information features include MOS type, curve type, operating voltage, device width, device length, operating temperature, conditions, and p-axis. The operating voltage is the working voltage of the semiconductor device. The device width is the width of the OD (Oxide Definition, the oxide layer used to define and isolate the active area, also known as the AA (Active Area, the region in the transistor where electron conduction actually occurs)). The device length is the length of the poly (poly is short for polysilicon, a key material in semiconductor manufacturing, mainly used to manufacture the gate of transistors). There are three p-axis values: the drain voltage, the bulk voltage, and the gate voltage. There are two conditions: the drain voltage and the bulk voltage. When the p-axis is the drain voltage, the condition is the bulk voltage; when the p-axis is the bulk voltage, the condition is the drain voltage. The voltage types of these two conditions are always different.

[0064] In one exemplary embodiment, such as Figure 2 As shown, a method for processing output data from a semiconductor machine is provided, which can be applied to... Figure 1 Taking the terminal in the example, the explanation includes the following steps S202 to S208. Wherein:

[0065] S202: Obtain the output data of the semiconductor equipment.

[0066] The output data includes measurement point information for each semiconductor element measured by the WAT machine. This measurement point information is used to generate the IV curve of the semiconductor element. The measurement point information is output according to the format of a data template, which includes different file headers and the corresponding data positions. The output data is output to the corresponding data positions according to the file headers.

[0067] S204: Load the output data into the first array of the target dimension.

[0068] In this embodiment, the output data can be loaded into the first array of the target dimension based on the type and quantity of the output data, where the data type is determined based on the information of each element in the file header, and the quantity is the number of output data corresponding to each data type.

[0069] The first array of the target dimension is generated based on the data type and the quantity of the output data, and the target dimension of the first array is determined based on the data type and the quantity of the output data. The target dimension can be constructed based on preset data characteristics of the output data, and the preset data characteristics include the data type and the quantity of the output data. The data type can be determined based on element information, and the element information includes a p-axis and a condition.

[0070] In some optional embodiments, the first array is generated based on the data type and the quantity of the output data, and then the output data is stored in the corresponding first array.

[0071] In some optional embodiments, storing the output data in the corresponding first array can include the following results: the first result is that the output data is stored in the first array; the second result is that the quantity of the output data is greater than the capacity of the first array, that is, some output data cannot be loaded into the first array; and the third result is that the quantity of the output data is less than the capacity of the first array, that is, the first array is not fully loaded. In this embodiment, if the second case or the third case occurs, an alarm information is output.

[0072] S206: The element information is obtained by classifying and analyzing the file header corresponding to the output data, and the element information is stored in the second array.

[0073] The output data corresponds to a data template, and the data template corresponds to each file header. The file header is also the file header corresponding to the output data.

[0074] The file header includes element information, and the element information includes a MOS type, a curve type, a working voltage, a device width, a device length, a working temperature, a condition, and a p-axis. The working voltage is the working voltage of a semiconductor element, the device width is the width of an oxide definition (OD) for defining and isolating an active area (AA), the device length is the length of a poly (poly is a short name of polysilicon, which is a key material in semiconductor manufacturing and is mainly used for manufacturing a gate of a transistor), the p-axis has three types, including a voltage value of a drain end, a voltage value of a bulk end, and a voltage value of a gate end, and the condition has two types, including a voltage value of a drain end and a voltage value of a bulk end. When the p-axis is the voltage value of the drain end, the condition is the voltage value of the bulk end; when the p-axis is the voltage value of the bulk end, the condition is the voltage value of the drain end; and the voltage types of the two are always different.

[0075] In this embodiment, the terminal categorizes and parses the file header corresponding to the output data to obtain the feature information in the file header, and stores the feature information in a second array. The dimensions corresponding to the second array are determined by the modeling engineer based on the data template corresponding to the output data. Storing the parsed feature information in this second array also includes three possible outcomes: the first is that the feature information is exactly stored in the second array; the second is that the number of feature information is greater than the capacity of the second array, meaning some feature information cannot be loaded into the second array; the third is that the number of feature information is less than the capacity of the second array, meaning the second array is not full. In this embodiment, if the second or third situation occurs, an alarm message is output.

[0076] S208: Merge the first array and the second array to obtain the target data.

[0077] The first array stores the corresponding output data, and the second array stores the corresponding file header element information. Combining the two arrays will yield the target data.

[0078] Specifically, the header information of the target data is obtained by filling in the corresponding element information values ​​based on the file type in the first array, and the content of the target data is obtained based on the output data in the first array. In this way, the target data can be obtained based on the header information and the content.

[0079] The above-mentioned semiconductor equipment output data processing method, after acquiring the output data of the semiconductor equipment, loads the output data into the first array of the target dimension; classifies and parses the file header corresponding to the output data to obtain element information, and stores the element information into the second array; merges the first array and the second array to obtain the target data. The entire process is automatically performed by the computer to obtain the target data, which improves the processing efficiency.

[0080] In one alternative embodiment, combined with Figure 3 As shown, Figure 3 This is a flowchart of a data filling step in one embodiment. In this embodiment, the data filling step, which loads the output data into a first array of the target dimension, includes:

[0081] S302: Extract the headers of each file in the output data, and count the different value combinations corresponding to the first element information group in the header to obtain each target combination.

[0082] The first element information group is used for screening the file header. In some optional embodiments, the first element information group includes name, device width, device length, and condition. The file header includes name, working voltage, device width, device length, working temperature, condition, and p-axis. In this embodiment, different value combinations corresponding to the first element information group in the file header are counted to obtain each target combination.

[0083] Specifically, the terminal extracts the file header of the output data, and counts the name, device width, device length, and condition in the file header. Different target combinations are obtained based on different values of the name, device width, device length, and condition.

[0084] In some optional embodiments, before extracting each file header in the output data, the method further includes: classifying the output data based on the working voltage of the semiconductor device.

[0085] In this embodiment, the output data is first grouped based on the working voltage, and then steps S302 to S308 are performed on the data in each group to store the output data into the first array, and steps S206 and S208 are performed to obtain the target data of the output data corresponding to the same working voltage.

[0086] S304: Dividing the output data into different first data groups based on the values of the second element information group in the output data, at least one element information in the first element information group and the second element information group being different.

[0087] The second element information group is used for extracting specific data in the output data. The processing of the file header based on the first element information group and the processing of the specific data based on the second element information group can be performed in parallel, i.e., the output data is processed through two aspects, and then the first array storing the output data is obtained based on the processing results of the two aspects. At least one element information in the first element information group and the second element information group is different, so that at least one element information is different, and the data grouping can be trimmed and screened through the target combination.

[0088] In some optional embodiments, the second element information set includes the device width, the device length, and the temperature, and the same device width, the same device length, and the same temperature in the output data are placed in a first data group in the embodiment. In some optional embodiments, the first data group is stored in the form of a file, that is, the output data in a first data group is placed in a text, so that a plurality of texts can be obtained, and the output data in each text has the same device width, the same device length, and the same temperature. In some optional embodiments, the name of the text can be named by the corresponding values of the device width, the device length, and the temperature, for example, W=0.5, L=0.9, T=25C_P5_P9_25C, wherein W is the device width, L is the device length, and T is the temperature.

[0089] S306: Obtain a preset data feature of the output data, and construct a first array based on the preset data feature.

[0090] The preset data feature is set in advance by a modeling engineer, and the modeling engineer gives the corresponding preset data feature for each data template, so that the first array can be constructed based on the preset data feature. For example, the number of rows and the number of columns of the first array are determined based on the preset data feature, and then the first array is initialized based on the number of rows and the number of columns.

[0091] In some optional embodiments, obtaining the preset data feature of the output data and constructing the first array based on the preset data feature includes: determining the number of rows of the first array based on the number of conditions corresponding to the output data and the number of rows of the output data corresponding to each condition; determining the number of columns of the first array based on the number of values corresponding to the variable element information corresponding to the output data, and the target dimension includes the number of rows and the number of columns; and constructing the first array based on the number of rows and the number of columns.

[0092] The preset data feature includes element information, and the element information includes conditions and variable element information, and the variable element information is a p-axis in the embodiment.

[0093] In the embodiment, the number of rows is determined based on the number of conditions and the number of rows of the output data corresponding to each condition, for example, the number of rows=C1*D1+C1, wherein C1 is the number of conditions, and D1 is the number of rows of the output data corresponding to each condition. The number of columns of the first array is determined based on the number of values corresponding to the variable element information, that is, the number of columns of the first array is determined based on the number of values of the p-axis, and specifically, the number of columns=L1+1, wherein L1 is the number of values of the p-axis.

[0094] In combination with Figure 4 as shown, Figure 4The first array corresponds to the case that C1=2, D1=2, and L1=2 in one embodiment. The number of columns is L1+1=2+1=3, because the first column is the step, and each of the remaining columns corresponds to a value of the p-axis. The number of rows is C1*D1+C1=2*2+2=6, where C1*D1 is the number of rows of the first array corresponding to all conditions, and C1 is the number of conditions, because each condition corresponds to a row for displaying information of the condition, i.e., the first row of the first array stores the file header corresponding to the first condition, and the remaining D1 rows store the output data corresponding to the first condition. The D1+2th row stores the file header corresponding to the second condition, and the following D1 rows store the output data corresponding to the second condition, and so on. That is, the first array includes at least one data block, and each data block includes D1+1 rows, where the first row stores the file header corresponding to the condition, and the second row to the D1+1th row stores the data corresponding to the condition.

[0095] S308: Process the first data group based on the target combination, and store the processed first data group into the first array.

[0096] The target combination is a combination of the file header, including the name, the device width, the device length, and the condition. The first data group stores the corresponding output data, which has the same device width, device length, and temperature. The first data group is processed based on the target combination, for example, the data in the first data group is cropped based on the name, the device width, the device length, and the condition, for example, the target combination corresponding to the same device width and device length as the data in the first data group is obtained, and then the data in the first data group is cropped based on the name and the condition in the target combination, so that the data in the first data group is grouped into second data groups with different names, device widths, device lengths, conditions, and temperatures. At this time, each second data group is different only in the p-axis.

[0097] In one optional embodiment, processing the first data group based on the target combination, and storing the processed first data group into the first array, includes: cropping the first data group based on the target combination to obtain a second data group; and storing the value of the target combination corresponding to the second data group and the second data group into the first array.

[0098] The terminal first crops the first data group based on the obtained file header (target combination), so as to obtain a second data group (only different in the p-axis); and then stores the value of the data combination in front, and the corresponding output data behind the value of the target combination.

[0099] Since the first data packet comprises at least one data block, each data block, the first row is used to store the file header information corresponding to the condition, and the second row to the D1+1 row is used to store the data corresponding to the condition, the second data packet can be stored in the corresponding data block in the first data packet.

[0100] In one of the optional embodiments, the file header in the output data is classified and parsed to obtain the element information, including: obtaining the constituting rule of the preset file header; based on the constituting rule of the preset file header, the file header corresponding to the output data is classified and parsed to obtain the element information.

[0101] The constituting rule of the preset file header can be a naming rule given by a modeling engineer. Based on the constituting rule of the preset file header, the element information corresponding to the header in the measurement point information is extracted (here, only the element, without value), and then the extracted element information is stored in the second array.

[0102] In one of the optional embodiments, the first array and the second array are merged to obtain the target data, including: extracting the value of the target combination corresponding to each second data packet from the first array; based on the value of the target combination corresponding to each second data packet and the element information in the second array, the header information of the target data is obtained; the corresponding second data packet is extracted from the first array as the content of the target data; based on the header information and the content, the target data is obtained.

[0103] The merging process is to generate the header of the target data and the content of the target data, wherein the header of the target data includes instruction information and element information value, and the instruction information is fixed, such as / / platform designautomation / / sweep data, which can be directly obtained by the terminal, and the element information value includes the value of the target combination corresponding to the second data packet and the element information in the second array, that is, the value of the element information in the second array is determined based on the value of the target combination corresponding to the second data packet, and is taken as the element information value of the header of the target data.

[0104] The content of the target data includes the data content in the corresponding data block in the first array, that is, the data in the second row to the D1+1 row of each data block in the first array.

[0105] For easy understanding, combined with Figure 5 The value of the element information in the first row of each data block in the first array is merged to obtain the header of the target data, and the data in the second row to the D1+1 row of each data block in the first array is taken as the content of the target data.

[0106] In the above embodiments, different types of measurement point information can be uniformly sorted and more standardized.

[0107] In one of the optional embodiments, the method further comprises: obtaining a data template and test data corresponding to the data template, the data template being used for a semiconductor machine to output corresponding output data; outputting a first prompt information that the data template is correct, in a case that the test data is successfully stored in the corresponding first array and second array; outputting a second prompt information that the data template has a problem, in a case that the test data is not successfully stored in the corresponding first array and second array, the second prompt information being used for correcting the data template; and obtaining output data of the semiconductor machine, including: obtaining output data of the semiconductor machine based on the correct data template.

[0108] In the embodiment, the data template corresponding to the semiconductor machine is tested before the semiconductor machine outputs data. Only the correct data template obtained by passing the test can be applied to the semiconductor machine to output data.

[0109] The test data corresponding to the test template is given by a modeling engineer based on the data template, which is simulation data and not actual output data of the machine. Therefore, the data template can be tested before the machine uses the data template. The test data is still processed by using the above steps S204 to S208, that is, the test data is processed and stored in the corresponding first array and second array. The specific processing method can be referred to the above description and will not be repeated here. If the test data is successfully stored in the corresponding first array and second array, it means that the data template is correct. If the test data is not successfully stored in the corresponding first array and second array, a second prompt information that the data template has a problem is output. The modeling engineer can correct the data template based on the output second prompt information, for example, the corresponding file header or the content of the corresponding output data can be corrected. For example, if the second array is not full, it means that the corresponding file header lacks or has redundant essential element information. Therefore, the file header in the data template is adjusted. If the data corresponding to a data block in the first array is not full or there is remaining data, the number of data lines of the corresponding data block in the data template is adjusted. In other embodiments, there can be other problems. The modeling engineer can correct the data template as needed until the data template passes the verification, and then the semiconductor machine uses the data template to output data.

[0110] In the above embodiments, the data template is verified before being used. When the data is not measured, the problem can be found and solved in time.

[0111] In one alternative embodiment, the method further includes: processing the target data using target software to obtain a target curve array.

[0112] The target software can be software used to generate the target curve array, such as MeQlab software. Importing the target data into the target software will yield the target curve array. For ease of understanding, the following is combined with... Figure 6 As shown, Figure 6 This is a schematic diagram of a target curve array in one embodiment. In this embodiment, each curve in the curve array includes different element information values. Each curve includes a curve corresponding to each p-axis value. The horizontal axis is Vgs and the vertical axis is Id, where Vgs is the gate voltage and Id is the drain current. For ease of understanding, the specific meanings of each parameter are given below. The horizontal axis, Vgs or Vg, represents the voltage value between the gate and source, in volts (V), and is a given input quantity. The vertical axis, ld, represents the drain current, in amperes (A), and is the output quantity measured from the chip. Vds represents the voltage value between the drain and source, in volts (V), and is another input quantity. Vbs represents the voltage value between the substrate and source, in volts (V), and is another input quantity. NMOS is an abbreviation for N-channel metal-oxide-semiconductor field-effect transistor. W is the gate width, in 1e-6 meters. L is the gate length, in 1e-6 meters. T is the temperature, in degrees Celsius. SA is the distance from the edge of the gate to the edge of the active area, in 1e-6 meters. SB is the distance from the edge of the gate to the other edge of the active area, in 1e-6 meters. A is a sequence number used to distinguish data under the same conditions, represented by A, B, C, and D respectively. The vertical axis lg represents the gate current, measured in amperes (A), which is the output quantity measured from the chip.

[0113] In the above embodiments, a unified method is used to quickly organize a large amount of measurement point information of different types, and it is easy to expand. The organized measurement point information can be seamlessly adapted for use in MeQlab. The process of organizing measurement point information is also a process of detecting the information, which can quickly identify problems in the original data and facilitate the troubleshooting of problematic data.

[0114] For ease of understanding, a complete embodiment is provided, which can be found in the following document. Figure 7 As shown, the output data of the semiconductor machine is first obtained according to the data template.

[0115] Second, the output data is grouped based on the operating voltage to obtain different output data groups. Then, each output data group is processed separately. The processing of each output data group can be performed in parallel to improve the overall data processing speed.

[0116] Third, extract the file header of each output data, and count the name, device width, device length and combinations of different conditions in the file header to obtain each target combination.

[0117] Fourth, divide the output data with the same device width, device length and temperature in each output data group into a first data group, so as to obtain a plurality of first data groups.

[0118] Fifth, based on each target combination, the data in each first data group is cut respectively to obtain a second data group.

[0119] Sixth, generate a first array based on the preset data characteristics given by the modeling engineer. The specific way to generate the target dimension of the first array can be referred to the above.

[0120] Seventh, store the data of the second data group and the corresponding target combination into the first array.

[0121] Eighth, classify and analyze the element information of the file header corresponding to the output data, and store the element information into a second array.

[0122] Ninth, combine the first array and the second array to obtain target data.

[0123] Tenth, input the target data into the MeQlab software to obtain an I_V curve array.

[0124] In the above embodiment, the data loading is realized by two rounds of arrays, and the unified data sorting process makes the information of different types of measurement points after sorting can be directly recognized by the MeQlab software, which improves the efficiency and is more standardized.

[0125] It should be understood that although each step in the flowchart involved in each of the above embodiments is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise stated herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each of the above embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be alternately executed with other steps or steps or stages in other steps.

[0126] Based on the same inventive concept, the embodiments of the present application further provide a semiconductor machine output data processing device for implementing the semiconductor machine output data processing method as described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more semiconductor machine output data processing device embodiments provided below can refer to the limitations of the semiconductor machine output data processing method described above, which will not be repeated here.

[0127] In one exemplary embodiment, as shown in Figure 8 a semiconductor machine output data processing device is provided, comprising: an output data acquisition module 801, a first array generation module 802, a second array generation module 803 and a merging module 804, wherein:

[0128] The output data acquisition module 801 is configured to acquire output data of a semiconductor machine.

[0129] The first array generation module 802 is configured to load the output data into a first array of a target dimension.

[0130] The second array generation module 803 is configured to classify and parse the file header corresponding to the output data to obtain element information, and store the element information into a second array.

[0131] The merging module 804 is configured to merge the first array and the second array to obtain target data.

[0132] In one optional embodiment, the first array generation module 802 is specifically configured to extract each file header of the output data, and count different value combinations corresponding to a first element information group in the file header to obtain each target combination; based on the value of a second element information group in the output data, the output data is divided into different first data groups, and at least one element information in the first element information group and the second element information group is different; a preset data feature of the output data is acquired, and the first array is constructed based on the preset data feature; the first data groups are processed based on the target combination, and the processed first data groups are stored into the first array.

[0133] In one optional embodiment, the first array generation module 802 is specifically configured to determine the number of rows of the first array based on the number of conditions corresponding to the output data and the number of rows of the output data corresponding to each condition; determine the number of columns of the first array based on the number of values corresponding to the variable element information corresponding to the output data, and the target dimension includes the number of rows and the number of columns; and construct the first array based on the number of rows and the number of columns.

[0134] In one of the optional embodiments, the first array generation module 802 is specifically configured to crop the first data packet based on the target combination to obtain a second data packet; and store the value of the target combination corresponding to the second data packet and the second data packet into the first array.

[0135] In one of the optional embodiments, the second array generation module 803 is specifically configured to obtain a constituting rule of a preset file header; and perform classified analysis on the file header corresponding to the output data based on the constituting rule of the preset file header to obtain element information.

[0136] In one of the optional embodiments, the merging module 804 is specifically configured to extract the value of the target combination corresponding to each second data packet from the first array; obtain the header information of the target data based on the value of the target combination corresponding to each second data packet and the element information in the second array; extract the corresponding second data packet from the first array as the content of the target data; and obtain the target data based on the header information and the content.

[0137] In one of the optional embodiments, the first array generation module 802 is further configured to classify the output data based on the working voltage of the semiconductor device.

[0138] In one of the optional embodiments, the apparatus further includes a test module configured to obtain a data template and test data corresponding to the data template, the data template being used for outputting the output data by the semiconductor machine; output a first prompt information indicating that the data template is correct in a case that the test data is successfully stored into the corresponding first array and second array; and output a second prompt information indicating that the data template has a problem in a case that the test data is not successfully stored into the corresponding first array and second array, the second prompt information being used for modifying the data template; and the output data obtaining module 801 is specifically configured to obtain the output data output by the semiconductor machine based on the correct data template.

[0139] In one of the optional embodiments, the apparatus further includes a curve generation module configured to process the target data by the target software to obtain a target curve array.

[0140] The modules in the semiconductor machine output data processing apparatus can be all or partially implemented by software, hardware, or a combination thereof. The modules can be embedded in or independent of a processor in a computer device in a hardware form, or stored in a memory in the computer device in a software form, so as to be called and executed by a processor to perform the operations corresponding to the modules.

[0141] In one of the exemplary embodiments, a computer device is provided, which can be a terminal, and an internal structure diagram of the computer device can be as shown in Figure 9The computer device shown in the figure includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be realized through WIFI, mobile cellular network, near field communication (NFC) or other technologies. The computer program is executed by the processor to realize a semiconductor machine output data processing method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0142] Those skilled in the art can understand that, Figure 9 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0143] In one embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to realize the steps in each of the above method embodiments.

[0144] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to realize the steps in each of the above method embodiments.

[0145] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to realize the steps in each of the above method embodiments.

[0146] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0147] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of each method. In the embodiments provided in the present application, any reference to memory, database or other medium can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0148] Any technical features in the above embodiments can be combined, and for the sake of brevity, not all possible combinations are described above, however, any combination of these technical features is deemed to be within the scope of the present application.

[0149] The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be pointed out that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for processing output data from a semiconductor machine, characterized in that, The method includes: Acquire the output data of the semiconductor equipment; Load the output data into the first array of the target dimension; The file header corresponding to the output data is classified and parsed to obtain feature information, and the feature information is stored in the second array; The first array and the second array are merged to obtain the target data, including: filling the corresponding element information values ​​based on the file type in the second array to obtain the header information of the target data; obtaining the content of the target data based on the output data in the first array; and obtaining the target data based on the header information and the content. Loading the output data into the first array of the target dimension includes: Extract the headers of each file in the output data, and count the different value combinations corresponding to the first element information group in the header to obtain each target combination; Based on the value of the second element information group in the output data, the output data is divided into different first data groups, and at least one element information in the first element information group and the second element information group is different. Obtain preset data features of the output data, and construct a first array based on the preset data features; The first data group is processed based on the target combination, and the processed first data group is stored in the first array.

2. The method according to claim 1, characterized in that, The step of acquiring preset data features of the output data and constructing a first array based on the preset data features includes: The number of rows in the first array is determined based on the number of conditions corresponding to the output data and the number of rows of output data corresponding to each condition; Based on the number of values ​​corresponding to the variable element information of the output data, the number of columns of the first array is determined, and the target dimension includes the number of rows and the number of columns; The first array is constructed based on the number of rows and the number of columns.

3. The method according to claim 1, characterized in that, The step of processing the first data group based on the target combination and storing the processed first data group into the first array includes: The first data group is cropped based on the target combination to obtain the second data group; The values ​​of the target combination corresponding to the second data group and the second data group are stored in the first array respectively.

4. The method according to claim 3, characterized in that, The step of classifying and parsing the file headers in the output data to obtain feature information includes: Obtain the default file header structure rules; Based on the preset file header composition rules, the file headers corresponding to the output data are classified and parsed to obtain element information.

5. The method according to claim 3, characterized in that, The step of merging the first array and the second array to obtain the target data includes: Extract the value of the target combination corresponding to each second data group from the first array; Based on the value of the target combination corresponding to each of the second data groups, and the element information in the second array, the header information of the target data is obtained; Extract the corresponding second data group from the first array as the content of the target data; The target data is obtained based on the header information and the content.

6. The method according to claim 1, characterized in that, Before extracting the file headers from the output data, the process further includes: The output data is classified based on the operating voltage of the semiconductor device.

7. The method according to claim 1, characterized in that, The method further includes: Obtain a data template and the corresponding test data, wherein the data template is used by the semiconductor equipment to output the output data; If the test data is successfully stored in the corresponding first and second arrays, output a first prompt message indicating that the data template is correct; If the test data is not successfully stored in the corresponding first array and second array, a second prompt message indicating that there is a problem with the data template is output. The second prompt message is used to correct the data template. The acquisition of output data from the semiconductor equipment includes: Obtain the output data of the semiconductor machine based on the correct data template.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: The target data is processed by the target software to obtain a target curve array.

9. A semiconductor machine output data processing device, characterized in that, The device includes: Output data acquisition module, used to acquire output data from semiconductor equipment; The first array generation module is used to load the output data into a first array of target dimensions, including: extracting the headers of each file of the output data, and counting the different value combinations corresponding to the first element information group in the file header to obtain each target combination; dividing the output data into different first data groups based on the values ​​of the second element information group in the output data, wherein at least one element information in the first element information group and the second element information group is different; obtaining preset data features of the output data, and constructing a first array based on the preset data features; processing the first data groups based on the target combinations, and storing the processed first data groups into the first array; The second array generation module is used to classify and parse the file header corresponding to the output data to obtain feature information, and store the feature information into the second array; The merging module is used to merge the first array and the second array to obtain target data, including: filling in the values ​​of corresponding feature information based on the file type in the second array to obtain the header information of the target data; obtaining the content of the target data based on the output data in the first array; and obtaining the target data based on the header information and the content.

10. The apparatus according to claim 9, characterized in that, The first array generation module is specifically used for: The first data group is cropped based on the target combination to obtain the second data group; The values ​​of the target combination corresponding to the second data group and the second data group are stored in the first array respectively.

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