Test and measurement system and method

Abstract

Reduce the exam time. [Solution] The test measurement system includes a test measurement device 10, a computing device 30 connected thereto, and one or more processors, the processors performing the following processes: collecting measurement data based on a full parameter sweep that includes all measurement points for all combinations of multiple parameters of the first device under test; identifying a core parameter sweep from the full parameter sweep that includes all measurement points for fewer parameter sweeps than the full parameter sweep; calculating an auxiliary parameter sweep using the core parameter sweep; determining a reduced parameter sweep using the core parameter sweep and the auxiliary parameter sweep; and later testing a device of the same type as the first device under test using the reduced parameter sweep.

Description

[Technical Field]

[0001] This disclosure relates to a test measurement system and method, and more specifically, to a technique for reducing parameter sweeps of a device under test. [Background technology]

[0002] Comprehensively investigating the parameter values ​​(e.g., voltage, current, etc.) of a device (e.g., an electronic device) by sweeping them across its parameter space is a crucial technique in design, characterization, and manufacturing processes. It is beneficial for verifying design integrity, deepening the understanding of device characteristics, and ensuring compliance with design specifications through calibration. This comprehensive approach allows engineers to identify and correct potential defects, optimize device performance for specific conditions, and adjust for manufacturing or environmental changes, ultimately ensuring that the product meets its intended specifications and operates reliably in its intended environment. This parameter sweep can be used, for example, in nonlinear calibration, bandwidth extension (BWE) characterization, and device noise characterization across various channel configurations. These techniques can be applied not only to the manufacture of the test measurement equipment itself, but also to the testing and calibration of the device under test (DUT) connected to the equipment. [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Special Publication No. 2024-508467 [Patent Document 2] Japanese Patent Publication No. 2020-190556 [Non-patent literature]

[0004] [Non-Patent Document 1] Introduction site of "Oscilloscope manufactured by Tektronix", Tektronix, [online], [searched on October 8, 2025], Internet <https: / / www.tek.com / ja / products / oscilloscopes>

Non-Patent Document 2

Non-Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0005] [[ID=十七]] This parameter sweep rapidly increases in complexity and duration as the number of parameters increases and the interval between their values (points) becomes finer. For example, to sweep two parameters at 10 points each, 10×10 = 100 combinations are required, but if it is extended to sweep three parameters at 20 points each, the number of combinations soars to 20×20×20 = 8,000 combinations. This exponential increase clearly shows the problem in comprehensively investigating the parameter space, that a slight increase in parameters or granularity significantly increases the required effort.

[0006] Since the test time gets longer and the cost gets higher, designers adopt various methods to minimize the number of sweeps. For example, they may select a specific set of sweeps from all the sweeps usually required by using the knowledge of that area, or select the necessary sweeps by using machine learning and AI based on a large-scale learning data set.

Means for Solving the Problems

[0007] This embodiment reduces the number of necessary parameter sweeps while maintaining the test criteria, thereby shortening the test time of the device under test (DUT). After analyzing the data of the full parameter sweep, the method of this embodiment selects a core parameter sweep which is a subset (minority group) within the full parameter sweep. Most of the information of the full parameter sweep is included in this core parameter sweep. Next, the method identifies a reduced set of non-core measurement points, which is referred to as an auxiliary parameter sweep in this application, i.e., a subset within the full parameter sweep that is not included in the core parameter sweep. The complete set of measurement points of the auxiliary parameter sweep can be predicted from the complete set of measurement points of the core sweep and the reduced set of measurement points of the auxiliary parameter sweep.

Brief Description of the Drawings

[0008] [Figure 1] FIG. 1 shows an embodiment of a test measurement system having a test measurement device and a device under test. [Figure 2] FIG. 2 shows an overlay plot of a full parameter sweep consisting of all measurement points of all parameter sweeps. [Figure 3] FIG. 3 shows a diagram of the configuration of a full parameter sweep. [Figure 4] FIG. 4 shows a plot of the logarithm scale of singular values based on the singular value decomposition (SVD) of a full parameter sweep. [Figure 5] FIG. 5 shows a plot of the normalized cumulative sum of singular values based on the singular value decomposition of a full parameter sweep. [Figure 6] FIG. 6 shows a core parameter sweep selected from a full parameter sweep. [Figure 7] FIG. 7 shows a parameter sweep with the least error. [Figure 8] FIG. 8 shows a parameter sweep with the largest error. [Figure 9] FIG. 9 shows a parameter sweep with improved accuracy. [Modes for carrying out the invention]

[0009] Embodiments disclosed herein describe a test measurement system and method for performing a parameter sweep of a device under test (DUT). Figure 1 shows a diagram of an embodiment of the test measurement system. This system includes a test measurement device 10 and a computing device 30. The system may also include a device under test (DUT) 14 separate from the test measurement device 10. In one embodiment, the test measurement device may be the DUT. In another embodiment, the test measurement device and the computing device may be the same device. The test measurement device may consist of an oscilloscope, a multimeter, a waveform analyzer, or other test equipment.

[0010] The test measurement device 10 generally includes one or more processors (e.g., 12), a user interface (U / I) 22, one or more ports (e.g., 16) for communicating with the DUT 14 of these embodiments, and one or more ports (e.g., 24) for communicating with the computing device 30. The test measurement device may also include one or more analog-to-digital converters (ADCs) 18 for converting signals from the DUT into digital data, and one or more memories 20 for storing data. One or more processors are configured to run programs that cause one or more processors to perform various tasks, according to embodiments disclosed herein. The computing device 30 also includes one or more ports (e.g., 36) for communicating with the test measurement device. This is illustrated as a direct wired connection, but may include network connections, wireless connections, etc. The computing device 30 also includes one or more processors 32, a user interface 34, and memory 38. One or more processors in the test measurement system run programs to perform tasks, which may reside in the test measurement device, in the computing device, or distributed between them. In some embodiments, the test measurement device collects measurements for a complete parameter sweep, a computing device performs a reduction in the number of parameter sweeps, and then the test measurement device uses the reduced (decremented) parameter sweeps for the device under test thereafter.

[0011] As used in this application, the term "full parameter sweep" refers to a set of data containing all measurement points for all parameter combinations, also known as a parameter sweep, used in testing a particular device or design. For example, if 10 measurement points are obtained for each of 12 parameter sweeps, a full parameter sweep consists of 120 measurement points, which is far fewer than the actual number of measurement points that occur. Each measurement point in each parameter sweep represents the process of setting up the DUT for that point, and then measuring all measurement points for that particular parameter sweep. Next, the parameter sweep to be measured is changed, and this process is repeated. The engineer needs to repeat this for all DUTs.

[0012] The method of this embodiment uses Singular Value Decomposition (SVD) in parameter sweep reduction (PSR) to identify the number of parameter sweeps that can be reduced. This method, referred to here as the explicit method, identifies the minimum and essential parameter sweep, which is referred here as the "core" parameter sweep. Next, this method approximates the remaining parameter sweeps (referred here to as auxiliary parameter sweeps) by a linear combination of the core parameter sweeps. The coefficients of the linear combination are determined by the diminishing set of measurement points.

[0013] The explicit method of PSR serves two purposes. The first purpose is to determine which parameter sweep is the core parameter sweep, which aggregates the majority of the information from the complete set of parameter sweeps, regardless of whether the application is design, characterization, or calibration. The second purpose is to find a solution that approximates the non-core parameter sweeps (referred to hereby as auxiliary parameter sweeps) as a linear combination of the core parameter sweeps. The coefficients of the linear combination are determined by a reduced set or group of measurement points (a diminished set). Because the linear combination incorporates all the core parameter sweeps, this method achieves higher accuracy compared to conventional approaches that derive each of the non-core parameter sweeps based on only one or two other parameter sweeps.

[0014] The parameter sweep data is organized into the form of matrix X, where each column represents a measurement point for one parameter sweep, and each row represents a measurement point for all parameter sweeps. The singular value decomposition (SVD) of matrix X is shown in Equation 1. [Mathematics 1] X = UΣV T Here, the size of X is n × m, the left singular vector U is a unitary matrix of size n × n, the singular value matrix Σ is of size n × m with singular values ​​on its diagonal, and the right singular vector V is a unitary matrix of size m × m. Typically, n is greater than m. n represents the number of measurement points collected in each parameter sweep. The data or measurement points are organized into a single column. m represents the number of parameter sweeps.

number

[0015] Next, this process calculates the accumulated sum of the singular values ​​and normalizes these singular values ​​relative to the total sum so that the final accumulated sum is 1. The user needs to select a threshold for the information coverage. For example, selecting 0.98 as the threshold indicates that 98% of the information is covered. Then, the first accumulated sum that exceeds this threshold is identified. If the first p singular values ​​achieve a sum that exceeds the threshold, then p parameter sweeps are selected as the core parameter sweeps.

[0016] Once the number of parameter sweeps is determined, the process continues to identify the core parameter sweeps that make up that number. Since V is a unitary matrix, multiplying both sides of equation 1 by V gives equation 3. [Math 3] XV=UΣ

[0017] Note that the singular values ​​from p+1 to m are much smaller than the singular values ​​from 1 to p, and contain only minimal information about the parameter sweep. As an approximation, in Equation 3, the singular values ​​from p+1 to m in the matrix Σ are set to 0. Then the columns p+1 to m of matrix Σ are zero vectors. On the right side of Equation 3, the columns p+1 to m of matrix UΣ are zero vectors. This is because when a matrix is ​​multiplied by a zero vector, it produces a zero vector. Since the submatrix containing columns p+1 to m on the right side of Equation 3 is equal to zero, the submatrix containing columns p+1 to m on the left side of Equation 3 should also be equal to zero.

[0018] Based on empirical knowledge, the following procedure is designed to identify the core parameter sweep and the auxiliary parameter sweep. First, this process identifies the columns in matrix X that contribute most to the columns p+1 through m on the left side of Equation 3, and these are approximated as zero-column vectors. These columns are the auxiliary parameter sweeps.

[0019] Next, this process is performed so that the submatrix V is obtained from column p+1 to m. AUXIt indicates that it is included as [Number 4] V AUX = V :,p+1:m And vector I including components from 1 to m - p AUX is initialized.

[0020] This process starts from the last column (rightmost column) of matrix V corresponding to the minimum singular value of matrix X. The process is iterated from the last column to the left column, which corresponds to the singular values that gradually increase. This iteration order is determined because the smaller the singular value, the closer it is to zero. AUX

[0021] This process finds the component with the largest absolute value in the last column of matrix V AUX and records the row index of this component.

[0022] Next, this process swaps the row of matrix V including the component with the largest absolute value AUX with the bottom row of matrix V AUX and also swaps the corresponding column of matrix X and the components of vector I AUX . Next, it subtracts the scaled last column, deletes the components of the last row of all columns except the last column, and updates (updates) matrix V AUX .

[0023] Next, this process moves from right to left to the next column of the updated matrix V AUX and repeats the process of finding the component with the largest absolute value in this next column. It records the row index of the identified component and, in the same way as for the last column, swaps the rows of matrix V AUX , the columns of matrix X and the components of vector I AUX . For example, when looking at the second - last column from the end, it swaps the row including the component with the largest absolute value of the updated matrix V AUX with the second - last row from the bottom. It performs the same processing as for the last column, deletes the components of the second - last row from the bottom of the left column, and updates matrix V AUX .

[0024] This process involves updating the matrix V AUX This process is repeated until all columns of the matrix are complete. The matrix X obtained after this process is X Switched This is expressed as (Switched). The V obtained after this process AUX In equation 5, V AUXSwitched It is expressed as I obtained after this process. AUX is, I AUXSwitched It is expressed as follows.

number

[0025] The process then proceeds to identify a reduced set of measurement points (a set of measurement points with a reduced number), which can be used to create the coefficients of a linear combination of auxiliary parameter sweeps.

[0026] Core parameter sweeps are identified by analyzing data from full parameter sweeps. For the same type of design or device, core parameter sweeps are performed for each design or device. Each of these core parameter sweeps covers the entire set of measurement points.

number

[0027] For example, as shown in Matrix 1 above, the second and fourth columns are core parameter sweeps. Each of these sweeps performs a complete measurement covering 12 measurement points.

[0028] This process proceeds to identify a reduced set of measurement points for the auxiliary parameter sweeps, based on the measurement points included in the core parameter sweeps. Two observations can be made in this process. First, the measurement points of each auxiliary parameter sweep can be approximated by a linear combination of the measurement points of the core parameter sweeps. Second, since there are p core parameter sweeps, a minimum of p measurement points are required to determine the coefficients of the linear combination of each auxiliary parameter sweep.

[0029] Each core parameter sweep has n measurement points, therefore X Core Selecting p measurement points with the best condition values ​​from a p×p submatrix containing p rows improves the numerical results. Note that the number of rows selected is the same as the number of core parameter sweeps. The problem of selecting p rows from n rows to obtain a submatrix with the best condition values ​​can be solved in one of several ways. One way is to perform an exhaustive search, in which case the process calculates the condition values ​​of all possible submatrices and selects the submatrix with the smallest condition value. Another way is to formulate the problem as an optimization problem and solve this optimization problem numerically. Yet another way is to use column permutation (pivoting) and QR factorization to find a second-best solution.

[0030] By utilizing QR factorization and column permutation, a solution can be obtained quickly with minimal processing time. In this method, the matrix X, which includes the core parameter sweep, is used.Core Perform a pivot QR decomposition on the transpose of . The pivot QR decomposition routine returns a permutation vector, and the first p elements of the permutation vector are selected as the indices for p rows. For example, matrix 1 shows an example where rows 5 and 11 are selected as a diminishing set of measurement points that can be used to create the coefficients of a linear combination.

[0031] Assume that a diminished set of measurement points has been identified, as shown in Matrix 1. This process involves calculating an approximation of the auxiliary parameter sweep from the core parameter sweep and the diminished set of measurement points.

[0032] The measurement points of the auxiliary parameter sweep can be approximated by linear combinations of the measurement points from the core parameter sweep. The coefficients of the linear combinations of each auxiliary parameter sweep can be obtained using the diminishing set of measurement points identified in the previous step. That is, solve for c from A and b in Equation 6 below, where A is the X consisting of the p rows identified earlier. Core This is a submatrix of . b is a vector containing the p measurement points of the auxiliary parameter sweep. c is a vector containing the linear combination coefficients. [Number 6] Ac=b

[0033] To improve approximation accuracy, if more than p measurement points are obtained, the least mean squared (LMS) solution can be found from Equation 6. The numerical examples described below include cases with more than p measurement points.

[0034] Once the coefficients of the linear combination are obtained from Equation 6, the complete set of measurement points for the auxiliary parameter sweep can be calculated from the following Equation 7. [Number 7] x AUX(:,i) =X Core c

[0035] This acquires all measurement points for the design or device, as shown in Matrix 2.

number

[0036] The total number of actual measurement points shown in matrix 1 is 2 × (12 + 10) - 2 × 2 = 40. The total number of measurement points shown in matrix 2 is 12 × 10 = 120. Therefore, the coefficient for reducing the measurement points of a complete parameter sweep to a reduced set of measurement points is 3, as shown by the ratio 120 / 40 = 3. The reduced set of measurement points is then used in subsequent devices under test of the same design or type.

[0037] In this numerical example, three parameters are swept. All measurement points from all parameter sweeps are plotted together in Figure 2. The horizontal axis represents the measurement point (number), and the vertical axis represents its value.

[0038] Figure 3 shows all parameter sweeps, with each dot representing one sweep for one combination of parameters.

[0039] Figures 4 and 5 show the singular values ​​and normalized cumulative sums based on Singular Value Decomposition (SVD). Points that provide 98% or more of the cumulative sum are marked with circles. This reflects the selection of 98% as the threshold, and it should be noted that this threshold can be set to a different value. These plots are illustrated for ease of understanding. If this process were running on a computing device, these plots could be created and saved, but the software would likely calculate them numerically.

[0040] In Figure 6, the selected core parameter sweeps are shown as dots. The number of dots in Figure 6 is significantly less than in Figure 3, demonstrating the effectiveness of parameter sweep reduction (PSR).

[0041] When using an explicit method to reduce parameter sweep (PSR), the measurement points of the auxiliary parameter sweep can be approximated by a linear combination of the measurement points of the core parameter sweep. Once all auxiliary parameter sweeps have been approximated, the prediction error (approximation error), defined as the difference between the approximate value and the actual measurement point, can be calculated for each of the auxiliary parameter sweeps. In this process, the magnitude of the error peaks for each parameter sweep is collected. The sweeps for the minimum and maximum peaks are shown in Figures 7 and 8. The circles in the figures indicate which measurement points are selected to calculate the coefficients of the linear combination in Equation 6.

[0042] It is important to note the relatively large approximation errors observed at isolated measurement points. To reduce these errors, the process can include these measurement points in the actual measurements of the auxiliary parameter sweep. The software identifies these measurement points with errors exceeding a certain threshold and includes the actual measurement points when calculating the auxiliary parameter sweep. This reduces the peak prediction error for the worst auxiliary parameter sweep, as shown in Figure 9.

[0043] One embodiment of this invention is an explicit method for parameter sweep reduction (PSR). Linear algebraic techniques such as singular value decomposition (SVD) or pivot QR decomposition are used to identify the core parameter sweep and the reduced set of measurement points (a set or group with a reduced number of measurement points). The explicit method for PSR is computationally efficient and can be used for subsequent designs or devices of the same type on which the PSR was performed. The total number of measurement points covering all parameter sweeps is significantly reduced, leading to faster manufacturing throughput, lower costs, and reduced power consumption. This method has a wide range of applications for existing and new devices, including design simulation, factory calibration, and signal path calibration (SPC) of equipment. Examples include SPC applications such as high-frequency calibration of oscilloscopes, nonlinear calibration, broadband waveguide calibration (BWE) characterization, and noise characterization of oscilloscopes.

[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 Virsatile 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 comprising a test measurement device, a computing device connected to the test measurement device, and one or more processors, the one or more processors being A process for collecting measurement data based on a full parameter sweep that includes all measurement points for all combinations of multiple parameters of the first device under test, A process to identify a core parameter sweep from the full parameter sweep that includes all measurement points of a smaller number of parameter sweeps than the full parameter sweep described above, The process of calculating auxiliary parameter sweeps using the core parameter sweep described above, The process of determining the reduced parameter sweep using the above core parameter sweep and auxiliary parameter sweep, The process involves later testing a device of the same type as the first device under test using the reduced parameter sweep described above. It is configured to execute a program that causes one or more of the above processors to perform the task.

[0050] Example 2 is the test measurement system of Example 1, wherein the device under test includes a test measurement device.

[0051] Example 3 is a test and measurement system according to either Example 1 or Example 2, wherein the device under test is separated from the test and measurement apparatus.

[0052] Example 4 is a test measurement system according to any of Examples 1 to 3, wherein the program that causes one or more processors to perform the process of identifying the core parameter sweep includes a program that causes one or more processors to perform the process of singular value decomposition on a measurement matrix consisting of n measurement values ​​× m parameters.

[0053] Example 5 is the test measurement system of Example 4, wherein the program that causes one or more processors to perform singular value decomposition includes a program that causes one or more processors to perform a process of identifying p parameter sweeps that include key information exceeding a selected threshold, which are included in the full parameter sweep.

[0054] Example 6 is the test measurement system of Example 5, wherein a program that causes one or more processors to perform a process to identify p parameter sweeps containing key information exceeding a selected threshold includes a program that causes one or more processors to perform a process to identify columns in the measurement matrix as the core parameter sweeps.

[0055] Example 7 is a test measurement system according to any of Examples 1 to 6, wherein a program that causes one or more processors to perform the process of calculating the auxiliary parameter sweep using the core parameter sweep includes a program that causes one or more processors to perform the process of generating the auxiliary parameter sweep using a linear combination of the core parameter sweeps.

[0056] Example 8 is a test measurement system of Example 7, wherein one or more processors perform a process to calculate the auxiliary parameter sweep using the core parameter sweep, and the program that causes one or more processors to perform a process to select p rows to be used in the linear combination from a measurement matrix of n measurement values ​​× m parameters.

[0057] Example 9 is a test measurement system of Example 8, wherein a program that causes one or more processors to perform the process of selecting p rows from a measurement matrix of n measurement values ​​× m parameters includes a program that causes one or more processors to perform the process of generating row indices for the p rows in the measurement matrix using QR decomposition and column permutations.

[0058] Example 10 is a test measurement system according to any of Examples 1 to 9, wherein one or more processors are further configured to execute a program that causes one or more processors to perform the following: a process to determine the error in the auxiliary parameter sweep and a process to identify isolated measurement points that have an approximate error exceeding a predetermined threshold.

[0059] Example 11 is the test measurement system of Example 10, wherein one or more processors are further configured to execute a program that causes the one or more processors to perform a process that includes measurements from isolated measurement points having an approximation error exceeding a predetermined threshold in order to determine the measurement points for the auxiliary parameter sweep.

[0060] Example 12 is a method, A process for collecting measurement data based on a full parameter sweep that includes all measurement points for all combinations of multiple parameters of the first device under test, A process to identify a core parameter sweep from the full parameter sweep that includes all measurement points of a smaller number of parameter sweeps than the full parameter sweep described above, The process of calculating auxiliary parameter sweeps using the core parameter sweep described above, The process of determining the reduced parameter sweep using the above core parameter sweep and auxiliary parameter sweep, Using the reduced parameter sweep described above, a process is performed to later test a device of the same type as the first device under test described above. It is equipped with.

[0061] Example 13 is the method of Example 12, wherein the process of identifying the core parameter sweep includes singular value decomposition of a measurement matrix consisting of n measurement values ​​× m parameters.

[0062] Example 14 is the method of Example 13, wherein the process of performing singular value decomposition includes the process of identifying p parameter sweeps that contain the key information included in the full parameter sweep.

[0063] Example 15 is the method of Example 14, wherein the process of identifying p parameter sweeps includes the process of identifying a column in the measurement matrix as the core parameter sweep.

[0064] Example 16 is a method of any of Examples 12 to 15, wherein the process of calculating the auxiliary parameter sweep using the core parameter sweep includes the process of generating the auxiliary parameter sweep using a linear combination of the core parameter sweeps.

[0065] Example 17 is the method of Example 16, wherein the process of calculating the auxiliary parameter sweep using the core parameter sweep includes the process of selecting p rows to be used in the linear combination from a measurement matrix of n measurement values ​​× m parameters.

[0066] Example 18 is the method of Example 17, wherein the process of selecting p rows includes generating row indices for the p rows in the measurement matrix using QR decomposition and column permutations.

[0067] Example 19 is a method of any of Examples 12 to 15, further comprising a process for determining the error in the auxiliary parameter sweep and a process for identifying isolated measurement points having an approximation error exceeding a predetermined threshold.

[0068] Example 20 is the method of Example 19, further comprising a process of determining the measurement points for the auxiliary parameter sweep using measurements from any isolated measurement points having an approximation error exceeding a predetermined threshold.

[0069] The aforementioned versions of the subject matter of this disclosure have many effects that have been described or will be apparent to those skilled in the art. Nevertheless, not all of these effects or features are required in all versions of the disclosed apparatus, system, or method.

[0070] In addition, the description of this application refers to certain features. It should be understood that the disclosures herein include all possible combinations of these particular features. Where a particular feature is disclosed in relation to a particular aspect or example, that feature may, to the extent possible, also be used in relation to other aspects and examples.

[0071] 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.

[0072] 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 invention. Therefore, the present invention should not be limited to anything other than the appended claims. [Explanation of symbols]

[0073] 10 Test and measurement device 12 processors 14. Device under test (DUT) 16 ports 18 Analog-to-Digital Converters 20 memory 22 User Interface 24 ports 30 Computing Devices 32 processors 34 User Interface 38 memory

Claims

1. A test and measurement system, Test and measurement device, A computing device connected to the test and measurement device, One or more processors and The system comprises, and one or more processors, A process for collecting measurement data based on a full parameter sweep that includes all measurement points for all combinations of multiple parameters of the first device under test, A process to identify a core parameter sweep from the full parameter sweep that includes all measurement points of a smaller number of parameter sweeps than the full parameter sweep described above, The process involves calculating the auxiliary parameter sweep using the core parameter sweep described above, The process of determining the reduced parameter sweep using the above core parameter sweep and auxiliary parameter sweep, The process involves later testing a device of the same type as the first device under test using the reduced parameter sweep described above. 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 measurement system according to claim 1, wherein the program that causes one or more processors to perform the process of identifying the core parameter sweep described above includes a program that causes one or more processors to perform the process of singular value decomposition on a measurement matrix consisting of n measurement values ​​× m parameters.

3. The test measurement system of claim 2, wherein the program that causes one or more processors to perform the process of singular value decomposition includes a program that causes one or more processors to perform the process of identifying p parameter sweeps that include the main information included in the full parameter sweep.

4. A program that causes one or more processors to perform the process of calculating the auxiliary parameter sweep using the core parameter sweep described above is: The process of generating the auxiliary parameter sweep using a linear combination of the core parameter sweeps described above, The process of selecting p rows to be used in the above linear combination from a measurement matrix of n measurement values ​​× m parameters. A test measurement system according to claim 1, comprising a program that causes one or more of the above-mentioned processors to perform the above.

5. The test measurement system according to claim 1, which causes one or more processors to perform the process of selecting p rows from a measurement matrix of n measurement values ​​× m parameters, and further includes a program that causes one or more processors to perform the process of generating row indices for the p rows in the measurement matrix using QR decomposition and column permutations.

6. One or more of the above processors, The process for calculating the error in the above auxiliary parameter sweep, A process to identify isolated measurement points with approximation errors exceeding a predetermined threshold, In order to determine the measurement points for the above auxiliary parameter sweep, a process is performed to include measurements from isolated measurement points that have an approximation error exceeding the predetermined threshold. The test measurement system according to claim 1, further configured to execute a program that causes one or more of the above-mentioned processors to perform the above.

7. A process for collecting measurement data based on a full parameter sweep that includes all measurement points for all combinations of multiple parameters of the first device under test, A process to identify a core parameter sweep from the full parameter sweep that includes all measurement points of a smaller number of parameter sweeps than the full parameter sweep described above, The process of calculating auxiliary parameter sweeps using the core parameter sweep described above, The process of determining the reduced parameter sweep using the above core parameter sweep and auxiliary parameter sweep, Using the reduced parameter sweep described above, a process is performed to later test a device of the same type as the first device under test described above. A test and measurement method comprising the following.

8. The test measurement method of claim 7, wherein the process for identifying the core parameter sweep described above includes singular value decomposition of a measurement matrix consisting of n measurement values ​​× m parameters.

9. The test measurement method of claim 8, wherein the process of performing singular value decomposition includes a process of identifying p parameter sweeps that contain the main information included in the full parameter sweep described above.

10. The test measurement method of claim 9, wherein the process of identifying p parameter sweeps includes the process of identifying a column in the measurement matrix as the core parameter sweep.

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