Method and device for processing semiconductor device model test data

Abstract

The present invention provides a method and apparatus for processing semiconductor device model test data. The method specifically includes: obtaining a data source set for executing tests on semiconductor devices and storing the data source set in an embedded database; wherein the data source set includes one or more of a test data set, a simulation data set, and a source screening data set; based on the data source set stored in the embedded database, using preset data presentation conditions and one or more preset data presentation condition options on a preset display interface, viewing and extracting the data source set, generating and presenting a custom data set, and fitting a device model of the semiconductor device based on the custom data set. The extracted custom data set is then graphically drawn and model extracted, and repeated iterative verification is performed through real-time updates of the test data set to obtain reliable model extraction parameters.

Description

Technical Field

[0001] The present invention relates to the field of electronic design automation, and in particular discloses a method and device for processing semiconductor device model test data. Background Art

[0002] The test data simulation of semiconductor devices is an important tool for analyzing the principles and performance of semiconductor devices. It has a wide range of applications and has become an integral part of computer-aided design of integrated circuits. This "transplantation" of instrument-based experiments to powerful computer platforms can save time and effort in product design and development, and will inevitably bring huge social and economic benefits to the development of the semiconductor industry. The so-called simulation design technology refers to the use of various physical and mathematical, static and dynamic, continuous and discrete models to visually reproduce the essential processes occurring in actual systems, and through experiments on system models to quickly and accurately predict and condense relevant physical laws and trend forecasts, thereby assisting our research on actual systems. Simulation design technology includes measurement, simulation, optimization and statistical analysis tools. With the help of simulation analysis, appropriate parameter indicators are selected, chip structure parameters are optimized, and efficient and reliable experimental design solutions are provided.

[0003] In the prior art, after the experiment of measuring the parameter data of a semiconductor device is completed, the obtained test data is exported as a text file, and the integrated circuit modeling software imports the text file for further fitting processing. This method cannot achieve comprehensive and real-time analysis of the data, resulting in the inability to timely discover abnormal conditions in the device measurement process, the inability to timely evaluate the reliability of the test data of the semiconductor device, that is, the physical experimental data, the inability to timely verify the credibility of the semiconductor device simulation model and results, and the inability to timely improve and optimize the semiconductor device data measurement solution.

[0004] Furthermore, since it is impossible to conduct comprehensive, real-time, and effective analysis and comparison of test data and simulation data during or after semiconductor device data measurement, the test data that has not been compared and evaluated increases the possibility of model extraction failure, resulting in a large amount of resource waste. Summary of the Invention

[0005] In order to address the deficiencies in the prior art, the present application provides a method and apparatus for processing semiconductor device model test data.

[0006] A first aspect of the present application provides a method for processing semiconductor device model test data, which may specifically include:

[0007] Acquire a data source set for performing a test on a semiconductor device, the data source set including one or more of a test data set, a simulation data set, and a source screening data set;

[0008] Save the data source set and store the data source set in the embedded database;

[0009] Preset data presentation conditions, based on the data source set stored in the embedded database, preset the selection of one or more preset data presentation conditions for the display interface;

[0010] Processing a data source set, extracting the data source set according to preset data presentation conditions, generating and presenting a custom data set, and fitting a device model of a semiconductor device based on the custom data set.

[0011] In a possible implementation of the first aspect of the present application, obtaining a test data set includes:

[0012] A test data set of the semiconductor device is obtained according to preset measurement parameters of the semiconductor device.

[0013] In a possible implementation of the first aspect of the present application, obtaining the test data set includes obtaining a simulation data set including:

[0014] The model library file of the device model of the semiconductor device is called for parsing, and a simulation data set obtained by model parsing and model simulation is obtained.

[0015] In a possible implementation of the first aspect of the present application, obtaining the test dataset includes obtaining a source screening dataset including:

[0016] A source screening data set is obtained based on measurement parameters and measurement routines of a semiconductor device measurement process.

[0017] In a possible implementation of the first aspect of the present application, obtaining the test data set includes storing the data source set in the embedded database, including:

[0018] Reading the data source set and storing it in the embedded database and generating a soft link file matching the embedded database, wherein the soft link file and the path of the data source set stored in the embedded database correspond to each other and have a first mapping relationship;

[0019] According to the first mapping relationship, the test data is edited and updated.

[0020] In a possible implementation of the first aspect of the present application, obtaining the test data set includes editing and updating the test data, including:

[0021] Receive a target user's access request for the soft link file;

[0022] If there is a need to edit and change the first mapping relationship, the first mapping relationship and the soft link file are updated;

[0023] When the target user reads and edits the updated soft link file, the updated soft link file is pointed to the path of the data source set stored in the embedded database according to the updated first mapping relationship.

[0024] In a possible implementation of the first aspect of the present application, obtaining the test data set includes selecting one or more preset data presentation conditions of a preset display interface, including:

[0025] The selection of one or more preset data presentation conditions of the display interface is preset according to the category of the semiconductor device, the data source set, and the data type of the semiconductor device.

[0026] In a possible implementation of the first aspect of the present application, obtaining the test data set includes extracting a data source set including:

[0027] Obtain test data of a semiconductor device;

[0028] In a case where the value of the test data of a certain semiconductor device is not empty, a data source set including the test data set is extracted.

[0029] In a possible implementation of the first aspect of the present application, obtaining the test data set includes extracting a data source set and further includes:

[0030] Obtain simulation data of a semiconductor device;

[0031] When the value of the simulation data of any semiconductor device corresponding to the simulation data set is not empty, a data source set including the simulation data set is extracted.

[0032] In a possible implementation of the first aspect of the present application, obtaining the test data set includes presenting a data source set including:

[0033] Generate a custom data set based on the extracted data source information, and generate a corresponding two-dimensional matrix based on the custom data set;

[0034] When the data corresponding to the two-dimensional matrix exists, the custom data set is displayed on the graphical interface through the preset graphical interface algorithm.

[0035] In a possible implementation of the first aspect of the present application, obtaining the test data set includes fitting a device model of the semiconductor device based on the custom data set, including:

[0036] When the custom data set includes a test data set and a simulation data set, obtaining error data between the test data and the simulation data;

[0037] A device model of the semiconductor device is fitted based on the error data.

[0038] In a possible implementation of the first aspect of the present application, obtaining the test data set includes fitting a device model of the semiconductor device according to the error data, including:

[0039] Adjusting preset parameters in a model file of a device model of the semiconductor device, obtaining multiple sets of error data, and performing iterative fitting on the device model of the semiconductor device;

[0040] When the extracted model reaches a predetermined accuracy threshold, predetermined parameters corresponding to the device model fitting are generated;

[0041] The error data is generated by the error between the simulation dataset and the test dataset.

[0042] A second aspect of the present application provides an apparatus for extracting semiconductor device model test data. The apparatus may include a non-transitory computer storage medium having one or more executable instructions stored thereon. After the one or more executable instructions are executed by a processor, the apparatus performs the following steps:

[0043] Acquire a data source set for performing a test on a semiconductor device, the data source set including one or more of a test data set, a simulation data set, and a source screening data set;

[0044] Save the data source set and store the data source set in the embedded database;

[0045] Preset data presentation conditions, based on the data source set of the embedded database, preset the selection of one or more preset data presentation conditions for the display interface;

[0046] Processing a data source set, extracting the data source set according to preset data presentation conditions, generating and presenting a custom data set, and fitting a device model of a semiconductor device using the custom data set.

[0047] The present invention has the following beneficial technical effects:

[0048] 1. This application sets up an embedded database to save test data sets and simulation data sets, and establishes a soft link file mapping the embedded database to achieve real-time viewing of data measurement results, changes to data measurement sets according to editing change requirements, and timely verification of the credibility of semiconductor device simulation models and results.

[0049] 2. Through the data source set saved in the embedded database, according to the extracted custom data set including the test data set and the simulation data set, the graphic drawing and model extraction of the extracted custom data set are realized. Through the real-time update of the test data set, repeated iterative verification is carried out to obtain reliable model extraction parameters, effectively reducing the number of experiments, lowering the data measurement cost of physical experiments, accelerating the development of complex products, and increasing the reliability and success rate of model extraction parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings.

[0051] Figure 1 According to an embodiment of the present application, a schematic flow chart of a method for processing semiconductor device model test data is shown;

[0052] Figure 2 According to an embodiment of the present application, a schematic diagram of a preset data presentation method is shown;

[0053] Figure 3 According to an embodiment of the present application, a system architecture diagram of a semiconductor device measurement fitting model tool is shown;

[0054] Figure 4 According to an embodiment of the present application, a schematic diagram of a device for processing semiconductor device model test data is shown. DETAILED DESCRIPTION

[0055] The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0056] As used herein, the term "including" and its variations represent open inclusion, i.e., "including but not limited to." Unless otherwise stated, the term "or" means "and / or." The term "based on" means "based at least in part on." The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment." The term "another embodiment" means "at least one additional embodiment." The terms "first," "second," etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0057] Specifically, Figure 1 According to some embodiments of the present application, a flowchart of a method for processing semiconductor device model test data is shown, specifically including:

[0058] Step 100: Obtain a data source set for performing tests on semiconductor devices. The data source set includes one or more of a test data set, a simulation data set, and a source screening data set. It is understood that model extraction for semiconductor devices in simulation software requires a source screening data set obtained by re-screening the test data set based on a test data set from physical experiments on the semiconductor device, a simulation data set obtained from a simulator, or measurement parameter settings during a measurement process. This allows for viewing, editing, and fitting test data based on the data included in the data source set.

[0059] Specifically, in order to realize the measurement of test data sets for parameter measurement of semiconductor devices, it is necessary to drive various instruments, including probe stations (Probe), switch matrices (Matrix), high-precision digital source meters (SMU), etc. In order to speed up measurements and reduce repeated settings, it is necessary to establish a measurement routine (Routine) library and the connection relationship between devices and instruments.

[0060] In some embodiments of the present application, taking wafer measurement as an example, it is necessary to first obtain the information of all Dies (a unit on the wafer, all units have the same structure) through the probe station, and at the same time define the device information in the Subdie (sub-unit of the Die), as well as the routine to be measured on each device, etc. By presetting the above parameter information, batch measurement of the entire wafer can be achieved, and a test data set based on the corresponding preset measurement parameters can be obtained.

[0061] In the above step 100, a test data set of the semiconductor device is obtained according to the preset measurement parameters of the semiconductor device. It is understandable that the test data for the execution of the semiconductor device can be a current-voltage curve, a capacitance-voltage (CV) test, and also includes the measurement of scattering parameters (S parameters) in the radio frequency field. Accordingly, the test data set can include one or more current-voltage curves (current is, for example, drain current Id, gate current Ig, body current Ib, source current Is, etc.) capacitance-voltage (CV) tests, S parameters and other measurements. Several test data obtained from batch measurement tests of semiconductor devices can constitute a test data set.

[0062] In step 100, the model library file of the semiconductor device is called to perform model parsing, and a simulation data set is obtained by simulating the model parsing with a simulator. It is understood that the semiconductor device model presets the parameter values ​​in the model and also presets a model library file in a format that meets the requirements. The model library file is parsed to obtain a model instance, that is, each model parameter value, and then a simulation data set is obtained through the simulator. Among them, the relevant algorithms or algorithm models are achievable by those skilled in the art with existing technologies. In actual selection, an existing algorithm can be selected according to the actual needs of the scenario.

[0063] In step 100, a source screening dataset is obtained based on the measurement parameters and measurement routines of the semiconductor device measurement process. It is understood that in order to further filter the test dataset based on the type of semiconductor device, the measurement parameters and measurement routines are set during the physical experiment of the semiconductor device, i.e., during the test data acquisition process, to further filter some of the test data as the source screening dataset.

[0064] In some embodiments of the present application, under the condition that the semiconductor device for obtaining test data in physical experiments is specifically a wafer, the structure of the wafer is characterized by using a hierarchical structure such as Die to Sub-die and then to device through the wafer, and a part of a wafer with dozens to hundreds of identical Dies is selected. Further, a part of each die containing several or dozens of different Sub-dies is selected, and each Sub-die has different devices. By selecting different numbers of Dies, Sub-dies, and devices, and selecting the type of specific test data for wafer parameter measurement through a measurement routine, the selection of a source screening data set is achieved.

[0065] Specifically, taking 40 semiconductor devices of different sizes (5 different widths and 8 different lengths) as an example, there are 4 different data types, namely corresponding measurement routines. At this time, the test data set can be regarded as a two-dimensional matrix with 40 rows and 8 columns. Each point on the matrix corresponds to a type of data of a device of one size. This matrix may not be a full matrix. For example, the data is not measured, or the measured data is abnormal and is excluded. In this case, the values ​​corresponding to these points in the two-dimensional matrix corresponding to the test data set are empty; this situation does not exist for simulation data. When simulation data is missing, the simulator can be called as needed to perform simulation to further obtain a simulation data set; for the source screening data set, the first 10 dies of the 40 semiconductor devices of different sizes can be selected from the aforementioned semiconductor devices, with the die index ranging from 0 to 9, and the first three sub-dies of each die selected. The sub-die sequence ranges from 0 to 2, and all devices on each sub-die are selected. If there are 4 devices, it is equivalent to finally selecting 10x3x4=120 devices as the source screening data set.

[0066] Step 200: Save the data source set and store it in the embedded database. It is understood that embedded databases are typically released together with applications, eliminating the need for a separate database server. This provides portability. When users need to change their business needs or store data, and only need to save a version of a similar or identical file, soft link files can be used to provide normal access to different requests, enabling data access.

[0067] In step 200, storing the data source set in the embedded database includes: reading the data source set stored in the embedded database and generating a soft link file that matches the embedded database, wherein the soft link file and the path of the data source set stored in the embedded database have a first mapping relationship corresponding to each other; and editing and updating the test data according to the first mapping relationship. It is understandable that in order to access the embedded database, update data, and refresh the corresponding data source set, it is necessary to establish a new soft link file based on the embedded database, wherein the soft link file is used to point to the target path of the data source set stored in the embedded database, establish the first mapping relationship, and implement real-time updating, editing, and display of the test data based on the change requirements of the first mapping relationship. Specifically, reading the data source set stored in the embedded database may include storing the measurement data source set in the embedded database.

[0068] Specifically, implementing the editing and updating of test data includes: receiving a target user's access request for a soft link file; in the case where there is an editing change requirement in the first mapping relationship, updating the first mapping relationship and the soft link file; when the target user reads the edited and updated soft link file, the updated soft link file is pointed to the path of the data source set stored in the embedded database according to the updated first mapping relationship. It can be understood that, in response to determining that there is a soft link identifier in the embedded database, the target user determines that the requested file is a soft link file for accessing the requested soft link file, and accesses the data source set file corresponding to the embedded database pointed to by the soft link file. The soft link file contains the path name of the target file or data source set corresponding to the embedded database pointed to, and is connected to the data source set file through a soft link method, so that the target user can implement the request for updating and editing the test data set corresponding to the soft link file based on the request for the soft link file.

[0069] Step 300: Preset data presentation conditions. Based on the data source set stored in the embedded database, one or more preset data presentation conditions are selected on the preset display interface. It is understood that there may be significant discrepancies between the test datasets from semiconductor device physics experiments and the simulation datasets obtained through model analysis using the model file library. Selecting one or more preset data presentation conditions on the preset display interface facilitates further review and verification of the test datasets.

[0070] In the above step 300, the selection of one or more preset data presentation conditions of the preset display interface includes: the selection of one or more preset data presentation conditions of the preset display interface according to the category of the semiconductor device, the data source set, and the data type of the semiconductor device. It can be understood that for the physical experiment test and simulation operation of the semiconductor device, there will eventually be a test data set and a simulation data set that can be output. When modeling the semiconductor device model, it is necessary to implement the model extraction of the semiconductor device based on the test data set and / or the simulation data set. Specifically, according to the data stored in the embedded database, according to the category of the semiconductor device, the data source set obtained in the simulation test of the physical experiment test set of the semiconductor device, and the data type information of the semiconductor device, the preset display interface can display the selection of one or more preset data presentation conditions. The specific selection items can be set according to the needs of the target user. The relevant algorithms or algorithm models involved in the model extraction can be implemented by those skilled in the art based on the existing technology. In actual selection, the application scenario can be selected according to actual needs, and no limitation is made here.

[0071] In some embodiments of the present application, the semiconductor device includes at least one or more devices from the following groups: MOSFET transistors, SOI transistors, FinFET transistors, BJT transistors, HBT transistors, TFT transistors, MESFET transistors, diode rectifiers, oscillators, light emitters, amplifiers, photometers and other devices.

[0072] Step 400: Processing a data source set, extracting the data source set based on preset data presentation conditions, generating and presenting a custom data set, and fitting a device model of the semiconductor device based on the custom data set. It will be appreciated that for real-time display of semiconductor device model data, specific information about the data source set and simulation data set stored in an embedded database can be generated based on the data requirements or model simulation testing or physical experimental testing of the semiconductor device, to enable fitting of the device model based on the custom data set.

[0073] Specifically, the custom data set may include data information such as a data source set and a simulation data set.

[0074] Specifically, test data of a certain semiconductor device is acquired; when the value of the test data of the certain semiconductor device is not empty, a data source set including the test data set is extracted.

[0075] Specifically, simulation data of a certain semiconductor device is acquired; and when the value of simulation data of any semiconductor device corresponding to the simulation data set is not empty, a data source set including the simulation data set is extracted.

[0076] In the above step 400, presenting the data source set includes: generating a custom data set according to the extracted data source information, and generating a corresponding two-dimensional matrix according to the custom data set;

[0077] When the data corresponding to the two-dimensional matrix exists, the custom data set is displayed on the graphical interface through the preset graphical interface algorithm.

[0078] It is understandable that if Figure 2 As shown, a schematic diagram of a preset data presentation method is shown. When the test data set and the simulation data set are used to display data, each data display page first presents a navigation page to display the preset data presentation conditions in this data display page, including the selected semiconductor device, data type, and the corresponding data source set, etc. The target user selects the semiconductor device and data type on the data display page, and the software queries the data source set to generate the corresponding two-dimensional matrix. If the data of the two-dimensional matrix exists, the preset graphical interface algorithm realizes the display of the custom data set on the graphical interface, or directly draws the corresponding chart according to the selected preset data presentation conditions.

[0079] In the above-mentioned step 400, fitting the device model of the semiconductor device based on the custom data set includes: when the custom data set includes a test data set and a simulation data set, obtaining error data between the test data and the simulation data; fitting the device model of the semiconductor device according to the error data, specifically, the value of the simulation data set obtained by the simulator will not be empty.

[0080] Further, adjusting preset parameters in a model file of a device model of the semiconductor device, obtaining multiple sets of error data, and performing iterative fitting on the device model of the semiconductor device;

[0081] When the extracted model reaches a predetermined accuracy threshold, preset parameters corresponding to the device model fitting are generated. The error data is generated by the error between the simulation data set and the test data set, that is, the error data between multiple sets of simulation data from the simulator and multiple sets of test data from the physical experiment.

[0082] In some embodiments of the present application, when the target user extracts a model based on a simultaneous test data set and a simulation data set, a graphical interface presents the error data of the test data set and the simulation data set in the corresponding test semiconductor device. The error can be calculated using the root mean square (RMS). The target user obtains the root mean square value, makes corresponding adjustments based on the RMS value, and further changes the preset measurement parameters of the semiconductor device model. At the same time, by continuously changing the preset measurement parameters, the test data set of the corresponding tested semiconductor device is optimized to reduce the RMS value so that the RMS value extraction model reaches the accuracy of the preset accuracy threshold, thereby generating the preset parameters corresponding to the device model fitting.

[0083] In some embodiments of the present application, the RMS value can be preset to 5%, and when the iterative optimization value meets 5%, the preset parameters corresponding to the device model fitting are generated, where the RMS value can be limited according to the needs of the target user or the type of semiconductor device, and no specific limitation is made here.

[0084] In the above steps 100-400, the semiconductor device test data set and its model extraction can be implemented based on different modules of the test tool. Specifically, Figure 3A system architecture diagram of a semiconductor device measurement fitting model tool is shown. The model tool may specifically include: a data processing layer 1, a data storage layer 2, and a data presentation layer 3. The data processing layer may process data from a test data set or from a simulation data set obtained by a model simulator. The processed data is stored in an embedded database, and data processing is performed through soft link files, that is, the data set is processed to obtain a custom data set for data presentation, graphics drawing, and data fitting through optimization iteration.

[0085] Figure 4 According to an embodiment of the present application, a schematic diagram of a device for processing semiconductor device model test data is shown, specifically comprising: a non-transitory computer storage medium having one or more executable instructions stored thereon, wherein after the one or more executable instructions are executed by a processor, the following steps are performed:

[0086] Acquire a data source set for performing a test on the semiconductor device, wherein the data source set includes one or more of a test data set, a simulation data set, and a source screening data set;

[0087] Saving the data source set and storing the data source set in an embedded database;

[0088] Preset data presentation conditions, and selection of one or more preset data presentation conditions for a preset display interface according to the data source set of the embedded database;

[0089] The data source set is processed, the data source set is extracted according to the preset data presentation condition, a custom data set is generated and presented, and a device model of the semiconductor device is fitted using the custom data set.

[0090] It can be understood that each functional module of the above-mentioned device executes the same step flow as the aforementioned semiconductor device model test data processing method, which will not be described in detail here.

[0091] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0092] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0093] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0094] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction includes one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart, can be implemented by a dedicated hardware-based system that performs the prescribed function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0095] While various embodiments of the present disclosure have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for processing semiconductor device model test data, characterized in that: The method includes: A data source set for performing tests on semiconductor devices is obtained, the data source set including: one or more of a test data set, a simulation data set, and a source screening data set. For parameter measurements of semiconductor devices to be achieved by measuring the test data set, various instruments are driven, including a probe station, a switch matrix, and a high-precision digital source meter, and a measurement routine library and a connection relationship between the device and the instrument are established. When the parameter of the semiconductor device to be measured is a wafer, the information of a unit Die on all wafers is first obtained through the probe station, and the device information in the sub-unit Sub-die of the Die and the routine to be measured on each device are defined. By presetting the above parameter information, batch measurement of the entire wafer is achieved, and a test data set based on the corresponding preset measurement parameters is obtained. Under the condition that the semiconductor device for obtaining test data in a physical experiment is specifically a wafer, the structure of the wafer is characterized by using a hierarchical structure from Die to Sub-die and then to device through the wafer. By selecting different numbers of Die, Sub-die, and number of devices, and selecting the type of specific test data for wafer parameter measurement through the measurement routine, the selection of the source screening data set is achieved. Saving the data source set and storing the data source set in an embedded database includes: reading the data source set stored in the embedded database and generating a soft link file matching the embedded database, wherein the soft link file and the path of the data source set stored in the embedded database correspond to each other and have a first mapping relationship; implementing editing and updating of test data according to the first mapping relationship, including: receiving an access request for the soft link file from a target user; updating the first mapping relationship and the soft link file when there is an editing change requirement for the first mapping relationship; when the target user reads the edited and updated soft link file, pointing the updated soft link file to the path of the data source set stored in the embedded database according to the updated first mapping relationship; Preset data presentation conditions, and selection of one or more preset data presentation conditions for a preset display interface according to the data source set stored in the embedded database; Process the data source set, extract the data source set according to the preset data presentation condition, generate and present a custom data set, and fit the device model of the semiconductor device based on the custom data set, and generate preset parameters corresponding to the device model fitting when the extraction model reaches the accuracy of a preset accuracy threshold, wherein the error data is generated by the error between the simulation data set and the test data set; the error is calculated using the root mean square, the target user obtains the value of the root mean square, and makes corresponding adjustments based on the root mean square error value to change the preset measurement parameters of the semiconductor device model. At the same time, by continuously changing the preset measurement parameters, the test data set of the corresponding tested semiconductor device is optimized to reduce the value of the root mean square error so that the value of the root mean square error extraction model reaches the accuracy of the preset accuracy threshold, thereby generating the preset parameters corresponding to the device model fitting.

2. The method for processing semiconductor device model test data according to claim 1, wherein: Acquiring the simulation data set includes: The model library file of the device model of the semiconductor device is called for parsing, and the simulation data set obtained by the model parsing and model simulation is obtained.

3. The method for processing semiconductor device model test data according to claim 1, wherein: The selection of one or more preset data presentation conditions of the preset display interface includes: The selection of one or more preset data presentation conditions of the display interface is preset according to the category of the semiconductor device, the data source set, and the data type of the semiconductor device.

4. The method for processing semiconductor device model test data according to claim 1, wherein: Extracting the data source set includes: Acquiring test data of a semiconductor device; In a case where a value of the test data of a certain semiconductor device is not empty, the data source set including the test data set is extracted.

5. The method for processing semiconductor device model test data according to claim 1, wherein: Extracting the data source set further includes: Acquiring simulation data of a semiconductor device; In a case where the value of the simulation data of any semiconductor device corresponding to the simulation data set is not empty, the data source set including the simulation data set is extracted.

6. The method for processing semiconductor device model test data according to claim 1, wherein: Presenting the data source set includes: Generate a custom data set according to the extracted data source information, and generate a corresponding two-dimensional matrix according to the custom data set; When data corresponding to the two-dimensional matrix exists, the custom data set is displayed on a graphical interface through a preset graphical interface algorithm.

7. A device for extracting semiconductor device model test data, characterized in that: The apparatus includes a non-transitory computer storage medium having one or more executable instructions stored thereon. After the one or more executable instructions are executed by a processor, the following steps are performed: A data source set for performing tests on semiconductor devices is obtained, the data source set including: one or more of a test data set, a simulation data set, and a source screening data set. For parameter measurements of semiconductor devices to be achieved by measuring the test data set, various instruments are driven, including a probe station, a switch matrix, and a high-precision digital source meter, and a measurement routine library and a connection relationship between the device and the instrument are established. When the parameter of the semiconductor device to be measured is a wafer, the information of a unit Die on all wafers is first obtained through the probe station, and the device information in the sub-unit Sub-die of the Die and the routine to be measured on each device are defined. By presetting the above parameter information, batch measurement of the entire wafer is achieved, and a test data set based on the corresponding preset measurement parameters is obtained. Under the condition that the semiconductor device for obtaining test data in a physical experiment is specifically a wafer, the structure of the wafer is characterized by using a hierarchical structure from Die to Sub-die and then to device through the wafer. By selecting different numbers of Die, Sub-die, and number of devices, and selecting the type of specific test data for wafer parameter measurement through the measurement routine, the selection of the source screening data set is achieved. Saving the data source set and storing the data source set in an embedded database includes: reading the data source set stored in the embedded database and generating a soft link file matching the embedded database, wherein the soft link file and the path of the data source set stored in the embedded database correspond to each other and have a first mapping relationship; implementing editing and updating of test data according to the first mapping relationship, including: receiving an access request for the soft link file from a target user; updating the first mapping relationship and the soft link file when there is an editing change requirement for the first mapping relationship; when the target user reads the edited and updated soft link file, pointing the updated soft link file to the path of the data source set stored in the embedded database according to the updated first mapping relationship; Preset data presentation conditions, and selection of one or more preset data presentation conditions for a preset display interface according to the data source set of the embedded database; Process the data source set, extract the data source set according to the preset data presentation condition, generate and present a custom data set, and use the custom data set to fit the device model of the semiconductor device, and generate preset parameters corresponding to the device model fitting when the extraction model reaches the accuracy of the preset accuracy threshold, wherein the error data is generated by the error between the simulation data set and the test data set; the error is calculated using the root mean square, the target user obtains the value of the root mean square, and makes corresponding adjustments based on the root mean square error value to change the preset measurement parameters of the semiconductor device model. At the same time, by continuously changing the preset measurement parameters, the test data set of the corresponding tested semiconductor device is optimized to reduce the value of the root mean square error so that the value of the root mean square error extraction model reaches the accuracy of the preset accuracy threshold, thereby generating the preset parameters corresponding to the device model fitting.

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