Test and measurement system and method

Abstract

Reduce the runtime of the test. [Solution] The test measurement system 20 includes a test measurement device 40 with a port for connecting to a device under test (DUT) 30, a generating AI model, a user interface 46 that allows the user to provide input to the system 20, and a processor 42. The system receives user input that specifies the test requirements, provides the test requirement specifications to the generating AI model, receives a set of preconditions from the generating AI model, uses the preconditions to generate a test sequence for the DUT, minimizes hardware changes for the test, minimizes the number of data collections from the DUT, and executes the test according to the sequence in which data is acquired from the DUT. The data acquired from the DUT is analyzed to determine whether the DUT passed or failed one or more tests.

Description

Technical Field

[0001] This disclosure relates to test measurement systems, particularly systems and methods for test runtime optimization of a device under test (DUT).

Background Art

[0002] Generally, the test process of a DUT is considered to be divided into two stages. First, creation including test description, and second, runtime for executing the test as described. Regarding using artificial intelligence (AI) to read a specification (specification information, or simply a specification) and generate a test program, many proposals have been made. Conventionally, emphasis has been placed on the code generation part of this process. FIG. 1 shows an example of a code generation workflow. The specification is used to generate code for executing tests by a generation AI model.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] Typically, testing involves a single description but multiple executions. Many traditional AI specification-to-code workflows focus on optimizing the time to code generation, the first step. However, reducing test runtime is a particularly important consideration for test measurement system customers. Faster testing means shorter test times. On a manufacturing line, faster testing increases throughput, which is often more important than test creation time. [Means for solving the problem]

[0006] One embodiment of this disclosure is the use of code generation to shorten test time. In addition to simply shortening the test execution time (runtime), these embodiments may reduce the number of tests that need to be performed and the amount of data required to analyze and determine whether the device under test (DUT) has passed the test. Passing the test means that the DUT has met the performance requirements defined in the specifications, such as standards, which will be explained in more detail later.

[0007] These embodiments incorporate AI into the test creation process by using a generative AI model (referred to as "AI" in this application). This allows for a unique perspective on the user's intent regarding the test. By leveraging AI, users of test executive software can achieve a form of global optimization that is not currently available. In this application, "test executive" means a software program or package that controls the test process. For example, test executive software may compare measurements from a DUT to limit values ​​and determine whether the DUT passes or fails. A test executive may consist of commercially available software programs or may include code written specifically for testing.

[0008] These embodiments take a different approach than simply generating code, allowing test executive software to apply diverse optimizations to the test suite. In the process of feeding the AI ​​a specification describing the requirements the DUT must meet, the AI ​​gains detailed knowledge of the test environment in which it will be performed. This knowledge is useful for providing the test executive with "hints" for optimization, reducing the amount of code to be generated. These hints can be considered "pre-requests," as shown in Figure 2. The test specification 10 is provided to the generating AI model (AI) 12. The AI ​​12 generates test code 14 and creates a list of pre-requests 18, which are then passed to the test executive 16.

[0009] A pre-request provides the test executive with prior knowledge that both the DUT and the instrument require a specific type of test setup, and that the analysis requires a specific type of data. The data required for the analysis may include specific measurements and other types of data collected during the test. [Brief explanation of the drawing]

[0010] [Figure 1] Figure 1 shows an example of a test workflow using artificial intelligence (AI). [Figure 2] Figure 2 shows an embodiment of a test workflow using AI. [Figure 3] Figure 3 shows an embodiment of a generalized test workflow for test setup. [Figure 4] Figure 4 shows another embodiment of the generalized test workflow. [Figure 5] Figure 5 shows an example of a generalized test workflow using prerequisite knowledge. [Figure 6A] Figure 6A shows the unoptimized test operation. [Figure 6B] Figure 6B shows the optimized test operation. [Modes for carrying out the invention]

[0011] Figure 3 shows an example of a test measurement system 20 according to an embodiment of the present disclosure, used in a generalized test workflow for test setup. The test measurement system 20 may include several different elements that constitute the test environment. These include, but are not limited to, one or more test measurement devices (e.g., 40), one or more devices under test (DUTs) 30, other components 22 (e.g., components such as a pressure chamber 24 or a temperature chamber 26), a separate computing device independent of the test measurement devices (e.g., 50), and one or more storage devices (e.g., 52).

[0012] The device under test (DUT) 30 must be set up or configured as part of the workflow in step 54. The DUT can be configured automatically or manually. In automatic configuration, one or more processors present in the test measurement system shown in Figure 3 can configure the DUT. The processors may be present in one or more test measurement devices (e.g., 40) or in one or more independent computing devices (e.g., 50), which may include independent computing devices for analysis and computing devices for running the generative AI model. Generally, the processors are configured to execute code (programs) that cause the processor to perform one or more tasks. Similar to the DUT, the test measurement device 40 for observing and capturing signals from the DUT during testing may also be configured automatically or manually as part of the workflow in step 56. The configuration of the DUT and the test measurement device 40 is performed according to the test specification ("Test Specification Information" or simply "Test Specification") 10 shown in Figure 2. As also shown in Figure 2, the test specification is provided to the generative AI model.

[0013] After setup / configuration, the workflow proceeds to step 58, where the signal is acquired (waveform data is obtained) in the form of digital data. The test measurement device 40 receives the signal from the DUT through one or more ports 32. One or more ports 32 may include probes, photoelectric converters, etc. This signal is sent to the sampler track and hold circuit 34. The track and hold circuit 34 holds each signal stably for a certain period of time, long enough to allow acquisition by one or more high-resolution analog-to-digital converters (ADCs) 36. The analog-to-digital converters (ADCs) 16 convert the analog signals from the track and hold circuit 34 into digital signals. One or more ADCs 36 generally consist of high-speed ADCs. The digital signals from the ADCs 36 are stored in the acquisition memory 38. In addition to the acquisition memory 38, the test measurement device 40 may also have memory 44. Memory 44 may store program instructions for one or more processors 42 and, if necessary, other data.

[0014] User input is received from the user interface 46 and supplied to one or more processors 42. The user interface 46 may include a keyboard, mouse, trackball, touchscreen, or other operating device that the user can use to interact with the system using a GUI (Graphical User Interface) on the display 48. The display 48 may be a digital screen or other monitor that displays waveforms, measurements, or other data to the user. The display and user interface may consist of a single device, such as a touchscreen, that enables user input, or a combination of a touchscreen and the other input devices described above.

[0015] One or more processors 42 can be configured to execute instructions from memory and may perform any method and related steps indicated by such instructions, which may include processes such as receiving signals obtained from the acquisition memory 38 and reconstructing the test signal from samples generated by the ADC.

[0016] The components of the test measurement device 40 are depicted as being integrated within a single device, but those skilled in the art will understand that any of these components may exist outside the test measurement device 40 and can be connected to the test measurement device 40 by any conventional method (e.g., wired or wireless communication media or communication mechanisms).

[0017] In some cases, as will be described in detail below, data may be transferred to a computing device 50 separate from the test measurement device 40. The conditions under which this transfer occurs will be described in more detail below.

[0018] The test specification 10 is provided to the generated AI model 12. The test specification may be specified by the user inputting it into the test measurement device 40 or the computing device 50. In some embodiments, the AI 12 may exist on the computing device 50 or, in some cases, on the network to which the computing device 50 is connected. In another embodiment, since the processing speed and computing power continue to improve, the test measurement device may be equipped with the AI 12 and data analysis capabilities. This test measurement device performs the test of the DUT and acquires data from the DUT in step 58 and enables analysis based on the test specification in step 62. The results and data are stored in the storage device 52 in step 64.

[0019] There are many defined tests, potentially numbering in the hundreds. Each one needs to be run, and the results interpreted. This is usually done by test executive software, which may be a commercially available product, but often, as mentioned above, is a temporary program (code) created specifically for this purpose. Writing code in this way limits optimization. Since specific tests are generally not designed with other tests in mind, it is not practical to share work already done across multiple tests.

[0020] Figure 4 shows a flowchart of the test workflow, but includes a different perspective. This diagram divides the problem into two parts: data generation and test operation. The data generation part includes setting up the DUT in step 54, setting up the test measurement equipment in step 56, data acquisition from the DUT in step 58, and, as described later, data transfer in step 60 based on the required connection speed. The core of the test operation consists of reporting the analysis in step 62 and saving test parameters, specifications, data, results, etc. in step 64.

[0021] Defining the required data describes the data needed for the test operation and connects the two parts mentioned above. In this explanation, this data is referred to as a "prerequisite" for performing the test operation. By having the AI ​​read the test specifications, the AI ​​can define the types required for these "prerequisites."

[0022] As a simple example, let's assume the user has specified that the test will be conducted under the DisplayPort 1.2 specification. This specification defines a set of "prerequisites" to be selected from the following: • Bitrate: 1.62, 2.7 Gb / s • Pattern format: None, D10.2, PRBS 7, Symbol Error Rate Pattern • Pre-emphasis: 0, 3.5, 6, and 9.5 dB • Differential level: 400, 600, 800, or 1200mV • Lanes: 1, 2, or 4 lanes (specifically, which set of lanes) The DUT and test measurement equipment are configured according to their specifications. Furthermore, the test defines other aspects of the required data. These typically affect how the data is acquired, and examples of required data, though not limited to, may include record length, number of unit intervals, and total number of specific signal patterns. Knowledge of the subject of the test and its requirements are communicated to the test executive, and a test sequence is used to minimize test time.

[0023] In any case, the specifications define a set of "prerequisites" that specify the key characteristics of the data provided in each test. These prerequisites apply to the DUT and associated equipment. There is a process to directly map the prerequisites to the DUT and equipment settings, in which case the DUT and equipment settings can be derived automatically.

[0024] Furthermore, saving "multiple assumptions" allows for comparison of these assumptions. This means that it becomes possible to notice if one assumption is used in multiple tests. By setting assumptions, the execution of the test can be viewed as shown in Figure 5. As shown in Figure 5, the assumptions completely define the setup for data generation, from DUT setup to acquisition and, if used, transfer. This makes data collection and reuse possible. This is beneficial because data collection is a costly process in terms of time, signal acquisition resources, and processing resources.

[0025] By defining test operations using preconditions, it becomes possible to parallelize operations that were previously not parallelizable. Roughly speaking, the analysis will be parallelized separately from the data acquisition stage. In other words, optimization will occur when the analysis is performed on a separate computing device 50, away from the test measurement device 40. This does not exclude optimization when the analysis is performed on the test measurement device itself. As mentioned above, with increased computing power, it may be possible for the test measurement device to handle all the related work. However, parallel processing is not typically performed when the analysis is performed on the test measurement device. The analysis system should be componentized to obtain maximum performance.

[0026] Furthermore, regarding the aforementioned connection speed, structuring the processing steps to achieve optimal performance may depend on the test environment. If there is a 10GbE bandwidth between the test measurement device and the external computing device and a high-speed connection is being used, it is reasonable to pull data to parallelize the analysis. On the other hand, if the connection is operating over a 100MbE network, transferring data may not be reasonable. Test executives should understand the environment and optimize the processing steps (schedule). However, this perspective on data movement offers even more opportunities for optimization.

[0027] The fact that AI extracts text information means that, as mentioned above, the task can be greatly simplified and focused by extracting the definitions of signals used in the test and providing that information to test executives who are aware of the preconditions. As a result, test executives can easily transition from unoptimized sets of behaviors to more optimized ones, as shown in Figures 6A and 6B.

[0028] Most test sequencers operate in an unoptimized state. The optimization approach shown in the embodiments of this disclosure significantly reduces the number of times DUT settings and test measurement equipment need to be changed. Note the thick black lines in Figure 6B between setup and test. These are waveform data that can be shared across consecutive tests. This significantly reduces the overall workload (and total test time) and enables the parallelization of multiple actions that could not be performed in parallel before.

[0029] Figure 6A shows an unoptimized test operation. Each test includes setting up the DUT, setting up the test measurement device, performing the test, and then analyzing the results. For example, by using prerequisites to identify shareable tests and data, the DUT setting only needs to be changed once, and each DUT setting only requires setup three times to perform six tests. As shown in Figure 6B, these two elements make it possible to perform six tests. For example, the DUT is set up twice instead of twelve times, the test measurement device is set up six times instead of twelve times, and the multiple tests are performed in an order that allows for these reductions.

[0030] In the example above, optimization was assumed to be based on the order of dependencies. However, this does not take into account the cost of the changes and other factors, which test executives should also consider. Returning to the test environment 20 in Figure 3, the setup of the environment may include elements other than the test measurement equipment and DUT. This may consist of other components 22 such as pressure chambers 24 and temperature chambers 26. For example, changing the pressure generally takes longer than changing the temperature, and both take much longer than changing the settings of the DUT or other test measurement equipment.

[0031] By knowing or predicting the time it takes for these elements to reach the required test setup, they can be optimized using change costs. Items that take a long time to set should first have their changes minimized. The same general methodology can also be used for measurement testing. By adding pressure and temperature to the list of prerequisites and knowing the cost to achieve each setting, equipment that takes a short time to set up can be prioritized over equipment that takes a long time to set up, thereby minimizing the impact of changes to equipment that takes a long time to set up.

[0032] Traditionally, optimizing the priority of DUTs, switches, and test / measurement devices involved paying attention to the order of dependencies. When dependencies are equal, for example, in a pressure / temperature chamber that changes both pressure and temperature, the priority is determined by the rate of change and the required achievement. Within the desired range, the slower rate of change is usually prioritized first; in this example, minimizing the pressure change, followed by minimizing the temperature change. The goal is always to minimize the overall setup time.

[0033] However, even this perspective may be too simplistic. For example, if achieving a temperature change is slow at low pressures, the pressure and temperature change minimization strategy may require more detailed modeling to determine the optimal sequence for minimizing the total time.

[0034] This type of optimization is achieved by adding additional characteristics, such as estimating the rate of change for configuration changes of environmental components. This gives test executives an additional opportunity to optimize the overall test time. This information may not necessarily be provided by AI, but will be associated with descriptions of configuration changes for the components involved. These estimates of change costs may also be added to the configuration of the test measurement equipment. If this information is available for various equipment, it becomes possible to analyze various equipment combinations to estimate typical test times. This allows users to select the best equipment and devices for their tests by estimating the overall test time based on optimized change cost estimates.

[0035] Additional aspects of the test environment to be considered in the optimization process may include minimizing wear on connectors, cables, and other accessories used in the test equipment, as well as optimizing for externally sensitive elements in terms of maintaining temperature stability (such as arranging to run the most sensitive tests last).

[0036] A test executive that recognizes preconditions possesses a unique and important characteristic: the preconditions are equipment-independent, and therefore, tests constructed in this manner can be run on compatible test hardware with appropriate conversion. U.S. Patent Application Publication No. 2018 / 0356445, published on 13 December 2018 (the contents of which are incorporated herein by reference), describes a system and method for performing effective measurements using “preconditions” of measurement. The test executive disclosed herein maps these preconditions to prerequisites and extends the use of these preconditions to more broadly optimizing test situations.

[0037] The embodiments described herein enable optimization in various ways, such as minimizing test time, interaction with the DUT, and test debugging. These embodiments take into account the rate of change in settings and the cost of data transfer from an optimization perspective. In other words, the optimization is adapted to each test environment, resulting in the best possible outcome for that environment.

[0038] Furthermore, these embodiments offer interactivity, allowing users to ask questions about test durations considering equipment sets and environments. This could answer questions such as, "What can be done with a 10GbE lab network?" or "How do Tektronix's 7 Series DPO and Keysight's MXR models work with this test suite?" Answering such questions will help in selecting where to invest in development and consistently outperform competitors in terms of test time.

[0039] In summary, in some embodiments of this disclosure, the AI ​​workflow used for reading specifications and creating test programs has been updated and modified as follows: These embodiments utilize a modified AI workflow for generating code from specifications. Multiple prerequisites are stored as part of the workflow that defines the data characteristics required for each test. Only the code (program) necessary to perform the analysis and interpret the results is generated. This significantly reduces the code required for the test.

[0040] A test executive can be created to set up the environment using equipment-independent preconditions and generate the data necessary for the test. This data is then entered into corresponding codes needed to evaluate the test conditions. Under this mechanism, it is possible to compare multiple preconditions for data reuse and parallel analysis.

[0041] Matching can be somewhat ambiguous, and may be reused, including in cases of partial matching. For example, all essential conditions except record length may match. Generally, when a shorter record length is required, a longer record length can be used by providing a subset, which is a less costly operation. Other similar ambiguous matchings are used. Test measurement equipment and devices may also include estimations of the rate of change (rate of change). Test executives can use this as input for optimization strategies. This information may also include LAN bandwidth, the performance of measurement equipment and offline analysis, and other considerations regarding test execution scheduling. This allows test executives to ensure that the best environmental performance is achieved.

[0042] These embodiments offer advantages such as reduced test time. Customers often choose vendors based on test speed. These embodiments enable an unparalleled level of optimization, a level of optimization not used in any existing product to the inventor's knowledge. These embodiments generate less code, which is excellent for AI learning and quality improvement. Less code also reduces the risk of bugs. These embodiments are device-independent. The preconditions define the shape of the signal, not how the signal behaves. These preconditions can be automatically translated into DUT and device settings.

[0043] Furthermore, these embodiments also provide automated input verification. Knowing the signal preconditions allows verification that the input data requirements are met. It may be important to be notified if the data set created by the test equipment does not conform to the standard. In such cases, even if the test would have passed if it conformed to the standard, it may be annotated as non-compliant or it may be indicated that manual setup was unsuccessful. These embodiments also provide a broad set of functions for the test executive. This approach enriches the function set of the accompanying test executive. This allows the test executive to minimize hardware configuration changes and observe where data can be reused. This significantly reduces equipment setup time and creates previously nonexistent parallelization opportunities. As a result, test time is significantly reduced. These embodiments can help customers choose better solutions by estimating test time considering various experimental conditions and equipment selections. It is also possible to estimate manufacturing throughput before constructing a test facility.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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

[0048] 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.

[0049] Example 1 is a test measurement system, A test measurement device having one or more ports that allow connection to one or more devices under test (DUTs), Generative AI models, A user interface that allows the user to input data into the above test measurement system, One or more processors and Equipped with, The one or more processors A process that receives user input to specify the test requirements, The process of providing the above test requirements specifications to the above generating AI model, The process involves receiving a set of preconditions for testing from the above-generated AI model, Using the above set of preconditions, a process is performed to generate a sequence of tests to be conducted on the above DUT, a sequence that minimizes hardware changes for the tests and minimizes the number of data acquisitions from the above DUT. The process involves performing a series of tests in the order in which data is acquired from the above DUT, The data obtained from the above DUT is analyzed, and a process is performed to determine whether the DUT has passed or failed one or more tests in a series of tests. It is configured to execute a program that causes one or more of the above processors to perform the task.

[0050] Embodiment 2 is the test measurement system of Embodiment 1, wherein one or more processors are further configured to execute a program that causes one or more processors to perform the process of setting up the DUT for testing.

[0051] Example 3 is a test and measurement system of Example 1 or 2, wherein one or more processors are further configured to execute a program that causes one or more processors to perform the process of setting up the test and measurement device for testing.

[0052] Example 4 is a test measurement system according to any of Examples 1 to 3, wherein one or more processors are further configured to execute a program that causes one or more processors to perform the process of setting up a test environment.

[0053] Example 5 is the test measurement system of Example 4, wherein the program that causes one or more processors to perform the process of setting up the test environment includes a program that causes one or more processors to perform the process of adjusting the order based on the time it takes to change the settings of at least one component of the test environment.

[0054] Example 6 is the test measurement system of Example 5, wherein the at least one component of the test environment includes at least one of a pressure chamber and a temperature chamber.

[0055] Example 7 is a test measurement system according to any of Examples 1 to 6, wherein the program that causes one or more processors to perform a process of analyzing data acquired from the DUT includes a program that causes one or more processors to perform a process of determining the data transfer capability within the test measurement system.

[0056] Example 8 is the test measurement system of Example 7, further configured such that one or more processors execute a program that causes the one or more processors to perform a process that transfers data from the test measurement device and analyzes the data in parallel, based on the data transfer capability within the test measurement system.

[0057] Example 9 is a test measurement system according to any of Examples 1 to 8, wherein the generative AI model consists of a generative AI model that has been trained to generate test preconditions from the input test requirements specifications.

[0058] Example 10 is a method, A process that receives user input to specify the test requirements, The process of providing the above test requirements specifications to the generating AI model, The process involves receiving a set of preconditions for testing from the above-generated AI model, Using the above set of preconditions, a process is performed to generate a sequence of tests to be conducted on the device under test (DUT), a sequence that minimizes hardware changes for the tests and minimizes the number of data acquisitions from the DUT. The process involves performing a series of tests in the order in which data is acquired from the above DUT, The process involves analyzing the data obtained from the above DUT to determine whether the above DUT has passed or failed one or more of the above series of tests. It is equipped with.

[0059] Example 11 is the method of Example 10, further comprising the process of setting up the DUT for testing.

[0060] Example 12 is the method of either Example 10 or 11, further comprising the step of setting up the test measuring device for testing.

[0061] Example 13 is a method of any of Examples 10 to 12, further comprising a process for setting up a test environment.

[0062] Example 14 is the method of Example 13, wherein the process of setting up the test environment includes a process of reordering the process based on the time it takes to change the settings of at least one component of the test environment.

[0063] Example 15 is the method of Example 14, wherein the at least one component of the test environment includes at least one of a pressure chamber and a temperature chamber.

[0064] Example 16 is a method of any of Examples 10 to 15, wherein the process of analyzing data acquired from the DUT includes a process of determining the data transfer capability of the test measuring device used to test the DUT.

[0065] Example 17 is the method of Example 16, further comprising a process that transfers data from the above-mentioned test and measurement device to enable parallel analysis.

[0066] Example 18 is one of the methods from Examples 10 to 17, further comprising the process of training the generating AI model to generate a set of test prerequisites from the specifications of the input test requirements.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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]

[0071] 10. Specifications (Specification Information) 12 Generative AI Models 14 Test Code 16 Test Executive Software 18 List of pre-requests 20 Test and Measurement Systems 22 Other Components 24 Pressure Chamber 26 Temperature chamber 30. One or more devices under test (DUT) 32 One or more ports 34 Sampler Track & Hold Circuit 36 High-Resolution Analog-to-Digital Converters 38. Acquisition Memory 40. One or more test and measurement devices 42 One or more processors 44 memory 46 User Interface 48 displays 50 Computing Devices 52 One or more storage devices

Claims

1. A test and measurement system, A test measurement device having one or more ports that enable connection to one or more devices under test (DUTs), Generative AI models and, A user interface that allows the user to input data into the above test measurement system, One or more processors and Equipped with, The one or more processors A process that receives user input to specify the test requirements, The process of providing the above test requirements specifications to the above generating AI model, The process involves receiving a set of test preconditions from the above-generated AI model, Using the above set of preconditions, a process is performed to generate a sequence of tests to be conducted on the DUT, a sequence that minimizes hardware changes for the tests and minimizes the number of data collections from the DUT. A process that performs a series of tests in the order in which data is acquired from the above DUT, The data obtained from the above DUT is analyzed, and a process is performed to determine whether the DUT passed or failed one or more tests in a series of tests. A test and measurement system configured to execute a program that causes one or more of the above-mentioned processors to perform the above task.

2. The test and measurement system according to claim 1, wherein one or more processors are further configured to execute a program that causes one or more processors to perform the process of setting up the DUT or the test and measurement device for testing.

3. The test measurement system according to claim 1, further configured such that one or more processors execute a program that causes one or more processors to perform a process of setting up the test environment by adjusting the order of operations based on the time it takes to change the settings of at least one component of the test environment.

4. The test measurement system according to claim 1, wherein the program that causes one or more processors to perform a process of analyzing data obtained from the above DUT includes a program that causes one or more processors to perform a process of determining the data transfer capability within the test measurement system, and a process of transferring data from the test measurement device based on the data transfer capability within the test measurement system so that the data can be analyzed in parallel.

5. The test measurement system according to claim 1, wherein the above-mentioned generating AI model is a generating AI model that has been trained to generate test preconditions from the specifications of the input test requirements.

6. A process that receives user input to specify the test requirements, The process of providing the specifications of the above test requirements to the generating AI model, The process involves receiving a set of test preconditions from the above-generated AI model, Using the above set of preconditions, a process is performed to generate a sequence of tests to be conducted on the device under test (DUT), a sequence that minimizes hardware changes for the tests and minimizes the number of data acquisitions from the DUT. A process that performs a series of tests in the order in which data is acquired from the above DUT, The process involves analyzing the data obtained from the above DUT to determine whether the above DUT has passed or failed one or more of the above series of tests. A test and measurement method comprising the following.

7. The test measurement method according to claim 6, further comprising a process for setting up the test environment by a process for adjusting the order based on the time it takes to change the settings of at least one component of the test environment.

8. The test measurement method according to claim 7, wherein the at least one component of the above-mentioned test environment includes at least one of a pressure chamber and a temperature chamber.

9. The process of analyzing the data obtained from the above DUT is: A process to determine the data transfer capability of the test measurement device used to test the above DUT. The test measurement method according to claim 6, further comprising a process for transferring the data from the test measurement device to enable parallel analysis of the data.

10. The test measurement method according to claim 6, further comprising a process of training the generating AI model to generate a set of test preconditions from the specifications of the input test requirements.

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