Test and measurement apparatus and method

Abstract

It handles a variety of applications without having to train multiple different models. [Solution] The test measurement device 30 includes a port that connects the test measurement device 30 to the device under test (DUT) 32 to enable receiving signals from the DUT 32, one or more analog-to-digital converters (ADCs) 38 that receive signals from the DUT 32 and convert them into one or more digital waveforms, a user interface 40 that allows the user to input queries, and one or more processors 42. The device is configured to execute a program that creates one or more images of one or more digital waveforms from one or more ADCs 38, sends one or more images to a domain-adaptive multimodal large-scale language model (MLLM), receives parameters from the domain-adaptive MLLM, provides the user with parameters for the DUT 32 according to the query, and has one or more processors 42 perform the processing of applying the parameters to the DUT 32.

Description

Technical Field

[0001] The present disclosure relates to a test measurement system, and more particularly to a test measurement system that uses machine learning (ML) to calibrate and measure a device under test (DUT).

Background Art

[0002] Communication systems generally include a transmitter and a receiver. As the signal speed increases, more complex equalizers are used in the transmitter and receiver to improve system performance. More optimization processes are required for transmitter tuning and receiver equalizer adaptation, and more calculations are required for system performance measurement. U.S. Patent Application Publication No. 2022 / 0373598 (issued on November 24, 2022, hereinafter referred to as the "'598 specification", the content of which is incorporated herein by reference in its entirety) discloses a short-pattern waveform tensor builder-based machine learning (ML) system, which can be used, in particular, for tuning and measurement of a device under test (DUT) such as a transmitter. The short-pattern waveform tensor builder-based ML system is designed to use a short-pattern waveform tensor to represent the characteristics of a long waveform obtained from a DUT, and then perform transfer learning to obtain a map from a two-dimensional tensor image to a measurement result to improve the measurement speed.

[0003] The short-pattern waveform tensor builder-based ML system has also been extended to other wired and wireless communication applications. For example, U.S. Patent Application No. 18 / 754,871 filed on June 26, 2024 (hereinafter referred to as the "'871 application", the content of which is incorporated herein by reference in its entirety) discloses a technique for extracting a plurality of linear approximation pulse responses from a waveform. For example, as shown in FIGS. 1 and 2, a plurality of linear approximation pulse responses (MLFPR: multiple linear fit pulse responses) are extracted from a PAM4 waveform.

[0004] As disclosed in U.S. Patent Application Publication No. 2024 / 0169210 (published May 23, 2024, hereinafter referred to as "Spec. 210," the contents of which are incorporated herein by reference in their entirety), extracted pulses and their vertical noise histograms can be incorporated into a three-dimensional tensor. In the three-dimensional tensor, the intensity (luminance) of a pixel represents the amplitude of a sample in the pulse or the number of hits in the vertical histogram. The three-dimensional tensor is represented in the form of an image, an example of which is shown in Figure 3. In this particular example, the multiple gray lines at the bottom (e.g., 14) represent higher amplitudes of linearly approximated pulses as the color darker. The line 12 at the top represents the histogram of the number of hits in the waveform represented in this tensor image. The current architecture uses a convolutional neural network (CNN) as shown in Figure 4, with the image in Figure 3 as the model input. The CNN has an input layer 20, hidden layers (e.g., 22), and an output layer 24.

[0005] This method utilizes transfer learning, as pre-trained CNN models like ResNet can extract features from images. The transfer learning process adapts the fully connected and output layers to create accurate mappings from input image data to corresponding labels, generating measurement results. This example of a machine learning system is adapted to handle calibration and characterization applications. [Prior art documents] [Patent Documents]

[0006] [Patent Document 1] U.S. Patent Application Publication No. 2022 / 0373598 [Patent Document 2] U.S. Patent Application Publication No. 2025 / 0004014 [Patent Document 3] U.S. Patent Application Publication No. 2024 / 0169210 [Non-patent literature]

[0007] [Non-Patent Document 1] Website introducing "Tektronix oscilloscopes," Tektronix, [online], [searched August 26, 2025], Internet<https: / / www.tek.com / ja / products / oscilloscopes> [Non-Patent Document 2] "CLIP: Connecting text and images", OpenAI, [online], [Accessed August 26, 2025], Internet<https: / / openai.com / index / clip / > [Overview of the project] [Problems that the invention aims to solve]

[0008] However, this ML system has its limitations. If different measurement, calibration, and characterization parameters are required for the same waveform, the ML model needs to be retrained or a different ML model needs to be built. [Means for solving the problem]

[0009] Embodiments of this invention include a novel system and method using short pattern waveform tensors and their derivatives, where all of the short pattern waveform tensors and their derivatives generate an image. These embodiments utilize text representing measurement, calibration, and characterization parameters.

[0010] Text-based large-scale language models (LLMs) possess vast knowledge and can, for example, summarize long texts. An LLM understands the short main points of a novel and how they are highly related to the entire novel. In addition to summarizing, a pre-trained LLM can also present alternative interpretations of a novel without additional training. Further development from LLMs has led to the emergence of multimodal large-scale language models. In this application, a "multimodal large-scale language model (MLLM)" refers to a model that integrates the functions of a large-scale language model, such as Chat-GPT, with other information formats (modalities) such as audio, video, and images, in addition to text. An example of this is the visual language model CLIP (Contrastive Language-Image Pretraining).

[0011] These embodiments perform domain adaptation on a multimodal large-scale language model (MLLM) that has been pre-trained based on publicly available information before adaptive processing. Additional domain-specific training creates a domain-adaptive base model for measurement, calibration, and characterization.

[0012] Currently available MLLMs contain a vast amount of knowledge in several information formats. Domain adaptation allows MLLMs to be adapted to specific domains, in addition to pre-training using generally available public data. Domain-adapted MLLMs possess higher intelligence and can produce more accurate results. These domain-adapted MLLMs can be deployed in the test measurement environment either as a local MLLM for the test measurement device or installed in a location accessible to the test measurement device. [Brief explanation of the drawing]

[0013] [Figure 1] Figure 1 shows an example of a quad-level pulse amplitude modulation (PAM4) waveform. [Figure 2] Figure 2 shows four examples of linearly approximated pulse responses from PAM4 waveforms. [Figure 3] Figure 3 shows an example of an image representing a three-dimensional tensor. [Figure 4] Figure 4 shows an embodiment of a convolutional neural network architecture. [Figure 5] Figure 5 shows an embodiment of a test measurement device. [Figure 6] Figure 6 shows an example of a current machine learning system. [Figure 7] Figure 7 shows an embodiment of a multimodal large language model (MLLM) for calibration, performance evaluation, and measurement during learning. [Figure 8] Figure 8 shows an embodiment of an MLLM model.

Best Mode for Carrying Out the Invention

[0014] Figure 5 shows an embodiment of a test measurement device 30. The device under test (DUT) 32 generates a waveform, which is captured by the test measurement device. This test setup may include a test measurement device 30 such as an oscilloscope. The test measurement device 30 receives signals from the DUT 32 directly or via a probe 34. When the DUT is an optical transmitter, the probe 34 typically consists of a test fiber connected to an optoelectronic converter and supplies signals to the test measurement device through one or more ports 36. Two ports may be used for differential signals, and one port may be used for single-ended signals. The signals are sampled and digitized by one or more analog-to-digital converters (ADCs) 38 to become digital waveforms.

[0015] The test measurement device has one or more processors represented by processor 42, a memory 44, and a user interface 40. The memory stores executable instructions in the form of code (program), and when executed by the processor, it causes the processor to perform tasks. Through the user interface 40 of the test measurement device, the user can interactively operate the test measurement device 30 to input settings, configure tests, create queries, etc., which will be described in detail later.

[0016] Embodiments in the present application use machine learning in the form of a machine learning system 48. The machine learning network may include a processor programmed with an MLLM that is part of the test measurement device or accessible to the test measurement device. As the capabilities of the test device and the processor evolve, one or more processors such as 42 may include both. The machine learning system may take the form of a programmed model operating on one or more processors.

[0017] In the current ML system as shown in FIG. 6, the ML network 54 learns about the tensor image 52, but for the calibration parameters, characteristic evaluation parameters, and measurement parameters 50 regarding the tensor image 52, there is a label (information indicating the correct answer of the data) 56, which is a simple vector. This results in a machine learning system effective for specific situations. For example, a certain ML model may be trained to output the tap values of a 5-tap optimal feed-forward equalizer (FFE). If the user desires the tap values of a 9-tap FFE, it is necessary to retrain the current model or for the user to build another ML system. This is because the current ML model is for 5 taps and 9 taps are completely different. The process of obtaining the FFE taps can consist of either measurements defined by the specifications of a document such as the IEEE800G / 800G Ethernet (registered trademark) standard or the characteristic evaluation of the taps.

[0018] An embodiment of the present invention provides a machine learning system 48 as a domain-adaptive MLLM. In the above example, if the domain-adaptive MLLM has been trained to generate a 5-tap FFE, it can understand that the encoded vectors of the 5-tap and 9-tap FFEs are close in terms of cosine distance. Therefore, the domain-adaptive MLLM can "infer" and output the value of the 9-tap FFE.

[0019] Figure 7 shows an embodiment of a domain-adaptive MLLM during training. The training process provides the domain-adaptive MLLM 70 with domain-specific training. According to some embodiments of this disclosure, the training process begins with feature extraction. Waveform features are extracted and represented as images 76 in a manner similar to current machine learning implementations, such as those shown in Figure 6. For example, pulses are extracted and represented as an image as shown in Figure 2 for the PAM4 signal in Figure 1.

[0020] These images are then used to create training datasets for specific domains. These images represent the characteristics of the dataset, and calibration parameters, characterization parameters, and measurement parameters are presented in text format. Each image has a corresponding text entry.

[0021] A pre-trained MLLM like CLIP is precisely tuned by adapting the text encoder / decoder 74 to match the image encoder 78 using a domain-specific data set. In Figure 7, the text encoder / decoder 74 operates as a text encoder. The alignment between the text modality model and the image modality model is measured. In one embodiment, cosine distance is used to measure the alignment of these two vectors. In cosine distance, -1 means that the vectors are pointing in opposite directions, and therefore, the measured value between aligned vectors is very close to or equal to 1. In matrix 80, the central values ​​where the image and text are aligned are patterned. Due to the alignment of these vectors, the values ​​at these intersections are high.

[0022] After the precise tuning of the model is complete, the domain-adaptive MLLM 70 can be used at runtime to generate results based on one or more input images 76 that have passed through the image encoder 78. This typically occurs using queries, as shown in Figure 8. The domain-adaptive MLLM can take feature-representing images as input and generate calibration parameters, characterization parameters, and measurement parameters based on user queries about the DUT received through the user interface of the test measurement device. In this case, the text constitutes the output rather than the input, so the arrows proceed in the reverse direction.

[0023] The text encoder / decoder 74 outputs text results based on a text vector equal to the encoded vector from the image input and query text. In Figure 8, the text encoder / decoder 74 functions as a text decoder. The query text contains types of calibration parameters, characterization parameters, and measurement parameters. The text encoder / decoder 74 interprets (translates) or decodes the text vector from the domain-adaptive MLLM and generates parameters related to the DUT.

[0024] Depending on the nature of the parameters, their application to the DUT can take various forms. For example, the parameters may consist of characterization parameters that describe the characteristics of the DUT. By comparing the DUT characterization with the design specifications, it is possible to determine whether the design meets the requirements or whether further design changes are needed during the design phase. The characterization may also include information provided during the tuning process, such as as part of the tensor image during the tuning process. If the parameters consist of calibration parameters, the device may adjust the DUT settings and other operating parameters to match the values ​​of the calibration parameters. If the parameters consist of measurements of the DUT, these measurements may be compared with test specifications (which may be the same or similar) rather than design specifications to determine whether the DUT passed or failed the test, i.e., whether the DUT conforms to the specifications. These are merely examples and do not limit the embodiments or claims.

[0025] Embodiments of this invention extend machine learning (ML) applications for calibration, characterization, and measurement to domain-adaptive MLLM. MLLM's text modality models possess extensive knowledge and intelligence, and excel at understanding the meaning of calibration parameters, characterization parameters, and measurement parameters. This enables domain-adaptive MLLM to handle diverse applications without requiring the training of numerous models using ML methods. Domain-adaptive MLLM possesses higher intelligence and can generate more accurate results.

[0026] Embodiments of the disclosed technology can operate on a specially programmed general-purpose computer, including specially created hardware, firmware, digital signal processors, or processors that operate according to programmed instructions. The terms “controller” or “processor” in this application mean microprocessors, microcomputers, ASICs, and dedicated hardware controllers, etc. Embodiments of the disclosed technology can be implemented by one or more computers (including monitoring modules) or other devices, using computer-readable data such as program modules and computer-executable instructions. Generally, program modules include routines, programs, objects, components, data structures, etc., which, when executed by a processor in a computer or other device, perform specific tasks or implement specific abstract data type expressions. Computer-executable instructions may be stored on computer-readable storage media such as hard disks, optical disks, removable storage media, solid-state memory, and RAM. As will be understood by those skilled in the art, the functions of the program modules may be combined or distributed as needed in various embodiments. Furthermore, these functions can be embodied in whole or in part in firmware or hardware equivalents such as integrated circuits or field-programmable gate arrays (FPGAs). One or more aspects of the disclosed technology can be more effectively implemented using specific data structures, such data structures are considered to be within the scope of computer-executable instructions and computer-usable data described herein.

[0027] The disclosed embodiments may, in some cases, be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored in one or more computer-readable media that can be read and executed by one or more processors. Such instructions may be referred to as computer program products. The computer-readable media described herein means any medium accessible by a computing device. For example, but not limited to, computer-readable media may include computer storage media and communication media.

[0028] Computer storage media means any medium that can be used to store computer-readable information. Examples of computer storage media include, but are not limited to, random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory and other memory technologies, compact disc read-only memory (CD-ROM), DVD (Digital Video Disc) and other optical disc storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices and other magnetic storage devices, and any other volatile or non-volatile removable or non-removable media implemented by any technology. Computer storage media exclude signals themselves and temporary forms of signal transmission.

[0029] A communication medium means any medium that can be used to transmit computer-readable information. Examples of communication mediums, though not limited to them, include coaxial cables, fiber optic cables, air, or any other medium suitable for transmitting electrical, optical, radio frequency (RF), infrared, sound, or other types of signals. Examples

[0030] The following examples are provided that are useful for understanding the technology disclosed herein. These embodiments may include one or more of the examples described below, or any combination thereof.

[0031] Embodiment 1 is a test measurement device comprising: a port that connects the test measurement device to a device under test (DUT) and enables it to receive signals from the DUT; one or more analog-to-digital converters (ADCs) that receive signals from the DUT and convert the signals into one or more digital waveforms; a user interface that enables a user to input queries; and one or more processors, the one or more processors being configured to execute a program that causes the one or more processors to perform the following processes: constructing one or more images of the one or more digital waveforms from the one or more ADCs; transmitting the one or more images to a domain-adaptive multimodal large-scale language model (MLLM); receiving parameters from the domain-adaptive MLLM; providing the user with parameters of the DUT in response to the queries; and applying the parameters to the DUT.

[0032] Example 2 is the test and measurement apparatus of Example 1, wherein the MLLM is present within the test and measurement apparatus.

[0033] Example 3 is a test and measurement apparatus according to either Example 1 or 2, wherein the MLLM is located remotely from the test and measurement apparatus.

[0034] Example 4 is a test and measurement apparatus according to any of Examples 1 to 3, wherein the above image is composed of either a 3D tensor or a 2D tensor.

[0035] Example 5 is a test measurement apparatus according to any of Examples 1 to 4, further configured such that one or more processors execute a program that causes one or more processors to perform a process of training the multimodal large-scale language model (MLLM) to create a domain-adaptive MLLM.

[0036] Example 6 is a test measurement apparatus of Example 5, wherein a program that causes one or more processors to perform a learning process for the MLLM includes a program that causes one or more processors to upload one or more learning data sets to the MLLM, each set having an image of features extracted from a waveform and corresponding text representing at least one of the calibration parameters, characteristic evaluation parameters, and measurement parameters of the waveform.

[0037] Example 7 is a test and measurement apparatus according to any of Examples 1 to 6, wherein the received parameters are characteristic evaluation parameters of the DUT, and the program that causes one or more processors to perform the process of applying the parameters to the DUT includes a program that causes one or more processors to perform the process of comparing the characteristic evaluation parameters of the DUT with the design specifications and determining whether the design satisfies the requirements of the design.

[0038] Example 8 is a test measurement apparatus of Example 7, wherein a program that causes one or more processors to perform a process of comparing the characteristic evaluation parameters of the DUT includes a program that causes one or more processors to perform a process of determining whether further design changes are necessary during the design phase.

[0039] Example 9 is a test and measurement apparatus according to any of Examples 1 to 8, wherein the received parameters are the measurement parameters of the DUT, and the program that causes one or more processors to perform the process of applying the parameters to the DUT includes a program that causes one or more processors to perform the process of comparing the measurement parameters with the specifications and determining whether the DUT is pass or fail.

[0040] Example 10 is a test and measurement apparatus according to any of Examples 1 to 9, wherein the above parameters include calibration parameters, and a program that causes one or more processors to perform the process of applying the above parameters to the DUT includes a program that causes one or more processors to perform the process of setting the parameters of the DUT to the values ​​of the calibration parameters.

[0041] Example 11 is a method comprising: a process of receiving a query from a user through the user interface of a test measurement device connected to a device under test (DUT); a process of receiving one or more digital waveforms from one or more analog-to-digital converters (ADCs) in the test measurement device; a process of creating one or more images of the one or more digital waveforms; a process of transmitting the one or more images to a domain-adaptive multimodal large-scale language model (MLLM); a process of receiving parameters from the domain-adaptive MLLM; a process of providing the user with parameters of the DUT in response to the query; and a process of applying the parameters to the DUT.

[0042] Example 12 is the method of Example 11, wherein the process of transmitting one or more images to the domain-adaptive MLLM includes the process of transmitting one or more images to the domain-adaptive MLLM on the test measurement device.

[0043] Example 13 is a method of either Example 11 or 12, wherein the process of transmitting one or more images to the domain-adaptive MLLM includes the process of transmitting one or more images to the domain-adaptive MLLM located remotely from the test measurement device.

[0044] Example 14 is a method of any of Examples 11 to 13, wherein the above image is composed of either a 3D tensor or a 2D tensor.

[0045] Example 15 is a method of any of Examples 11 to 14, further comprising a process of training the multimodal large-scale language model (MLLM) to create the domain-adaptive MLLM.

[0046] Example 16 is the method of Example 15, wherein the process for training the MLLM includes: extracting features from a set of waveforms and generating a set of features; creating a set of images representing the features; creating one or more training data sets including the set of images, at least one of calibration parameters, characteristic evaluation parameters, and measurement parameters, and corresponding text for each of the images; and uploading the one or more training data sets to the MLLM for creating the domain-adaptive MLLM.

[0047] Example 17 is a method of any of Examples 11 to 16, wherein the above parameters include characteristic evaluation parameters, and the process of applying the above parameters to the DUT includes a process of comparing the above characteristic evaluation parameters with the design specifications to determine whether the design satisfies the requirements of the design.

[0048] Example 18 is the method of Example 17, and also includes a process for determining whether further design changes are necessary.

[0049] Example 19 is a method of any of Examples 11 to 18, wherein the above parameters include measurement parameters, and the process of applying the above parameters to the DUT includes a process of comparing the above measurement parameters with the test specifications and determining whether the DUT passes or fails.

[0050] Example 20 is a method in which the above parameters include calibration parameters, and the process of applying the above parameters to the DUT includes the process of setting the parameters of the DUT to the values ​​of the calibration parameters.

[0051] All functions disclosed in the specification, claims, abstract and drawings, and all steps in any method or process disclosed, may be combined in any combination, except where at least some of such functions or steps are mutually exclusive. Each of the functions disclosed in the specification, abstract, claims and drawings may be replaced by an alternative function that serves the same, equivalent or similar purpose, unless otherwise specified.

[0052] In addition, the description in this application refers to specific features. The technologies disclosed herein should be understood to include all possible combinations of these specific features. For example, if a particular feature is disclosed in relation to a particular form, that feature may also be available in relation to other forms, as far as possible.

[0053] Furthermore, when this application refers to a method having two or more defined steps or processes, these defined steps or processes may be performed in any order or simultaneously, as long as the circumstances do not rule out such possibilities.

[0054] For the sake of explanation, specific embodiments of the present invention have been illustrated and described, but it should be understood that various modifications are possible without deviating from the gist and scope of the present invention. Therefore, the present invention should not be limited to anything other than the appended claims. [Explanation of symbols]

[0055] 30 Test and measurement equipment 32 Device under test 34 probes 36 One or more ports 38 ADC 40 User Interface 42 One or more processors 44 memory 48 Machine Learning Systems 50 Calibration parameters, characterization parameters, and measurement parameters 52 Tensor Images 54 ML Network 56 Labels 70 Domain-Adaptive MLLM 72 Calibration parameters, characterization parameters, and measurement parameters 74 Text Encoders / Decoders 76 Input Images 78 Image Encoders

Claims

1. A test and measurement device, A port that connects the test measurement device to the device under test (DUT) and enables it to receive signals from the DUT, One or more analog-to-digital converters (ADCs) that receive a signal from the above DUT and convert the signal into one or more digital waveforms, A user interface that allows users to enter queries, Equipped with one or more processors, The one or more processors are A process for constructing one or more images of one or more digital waveforms from one or more ADCs, The process involves sending one or more of the above images to a domain-adaptive multimodal large-scale language model (MLLM), The process of receiving parameters from the above domain-adaptive MLLM, The process of providing the user with the parameters of the above DUT in response to the above query, The process of applying the above parameters to the above DUT and A test and measurement device configured to execute a program that causes one or more of the above-mentioned processors to perform the above-mentioned task.

2. The test and measurement apparatus according to claim 1, further configured to execute a program that causes one or more processors to perform a process of training the multimodal large-scale language model (MLLM) to create a domain-adaptive MLLM by uploading one or more training data sets to the MLLM, each set having an image of features extracted from a waveform and corresponding text representing at least one of the calibration parameters, characteristic evaluation parameters, and measurement parameters of the waveform.

3. The test and measurement apparatus according to claim 1, wherein the received parameters are characteristic evaluation parameters of the DUT, and the program that causes one or more processors to perform a process of applying the parameters to the DUT includes a program that causes one or more processors to perform a process of comparing the characteristic evaluation parameters of the DUT with the design specifications and determining whether the design satisfies the requirements of the design.

4. The test and measurement apparatus according to claim 3, wherein a program that causes one or more processors to perform a process of comparing the characteristic evaluation parameters of the above-mentioned DUT includes a program that causes one or more processors to perform a process of determining whether further design changes are necessary at the design stage.

5. The test and measurement apparatus according to claim 1, wherein the parameters to be received are the measurement parameters of the DUT, and the program that causes one or more processors to perform a process of applying the parameters to the DUT includes a program that causes one or more processors to perform a process of comparing the measurement parameters with specifications and determining whether the DUT is pass or fail.

6. The test and measurement apparatus according to claim 1, wherein the above parameters include calibration parameters, and a program that causes one or more processors to perform a process of applying the above parameters to the DUT includes a program that causes one or more processors to perform a process of setting the parameters of the DUT to the values ​​of the calibration parameters.

7. A method for a test measurement device, The process of receiving queries from the user through the user interface of the test measurement device connected to the device under test (DUT), The process of receiving one or more digital waveforms from one or more analog-to-digital converters (ADCs) in the above-mentioned test measurement device, The process of creating one or more images of one or more digital waveforms as described above, The process involves sending one or more of the above images to a domain-adaptive multimodal large-scale language model (MLLM), The process of receiving parameters from the above domain-adaptive MLLM, The process of providing the above DUT parameters to the above user in response to the above query, The process of applying the above parameters to the above DUT and A method for a test and measurement apparatus equipped with the following.

8. A process to extract features from a set of waveforms and generate a set of those features, The process of creating a set of images that represent the above characteristics, The process of creating one or more training data sets, each containing the above set of images, at least one of the calibration parameters, characteristic parameters, and measurement parameters, and corresponding text for each of the above images, The process of uploading one or more of the above training data sets to the above MLLM in order to create the above domain-adaptive MLLM, and The method for a test measurement apparatus according to claim 7, further comprising a process of training the multimodal large-scale language model (MLLM) to create the domain-adaptive MLLM.

9. The method for a test and measurement apparatus according to claim 7, wherein the above parameters include characteristic evaluation parameters, and the process of applying the above parameters to the DUT includes a process of comparing the characteristic evaluation parameters with the design specifications to determine whether the design satisfies the requirements of the design.

10. The method for a test and measurement apparatus according to claim 7, wherein the above parameters include calibration parameters, and the process of applying the above parameters to the DUT includes the process of setting the parameters of the DUT to the values ​​of the calibration parameters.

Images