Calibration Alignment Judgment Method, Device, Testing Machine, and Electronic Device

Abstract

The present application relates to a calibration alignment judgment method, apparatus, tester, and electronic device. The method includes: obtaining the jitter distribution data of multiple signals to be calibrated and judged for alignment; based on the jitter distribution data of each signal, obtaining the probability density of the time difference between the signals; combining the probability density of the time difference and a set time difference range to obtain the probabilities inside and outside the theoretical range of the time difference between the signals; performing classification statistics according to the measured time difference between the signals and the time difference range to obtain the probabilities inside and outside the actual range of the time difference between the signals; and performing signal alignment judgment according to the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range. Comparing the probabilities inside and outside the actual range of the time difference between the signals obtained by measurement and statistics with the probabilities inside and outside the theoretical range as the basis for judging whether the edge time means of the signals are aligned, which can be applicable to jitters in different situations, reduce the influence of jitter calibration alignment, and improve calibration reliability.

Description

Technical Field

[0001] The present application relates to the technical field of semiconductor testing, and particularly to a calibration alignment judgment method, apparatus, tester, and electronic device. Background Art

[0002] In digital circuit testing, the output signals of a tester need to be synchronized to ensure that operations on all test channels are carried out under the same time reference, thereby avoiding test errors caused by inconsistent timing. To ensure output signal synchronization, the output signals of the tester need to be calibrated and aligned with high precision. In traditional signal calibration schemes, a step signal (i.e., a single rising edge or falling edge) is usually used to measure the deviation (skew) between channels, and the signals between different channels are aligned according to the measured deviation value. In this scheme, each measurement is affected by signal jitter, and the measurement results have a large deviation. Calibration based on this measurement result is greatly affected by signal jitter, and there is a disadvantage of low calibration reliability. Summary of the Invention

[0003] Based on this, it is necessary to provide a calibration alignment judgment method, apparatus, tester, and electronic device that can improve calibration reliability for the above problems.

[0004] The first aspect of the present application provides a calibration alignment judgment method, including:

[0005] Obtaining the jitter distribution data of multiple signals to be calibrated and judged for alignment;

[0006] Based on the jitter distribution data of each signal, obtaining the probability density of the time difference between the signals;

[0007] Combining the probability density of the time difference and a set time difference range to obtain the probabilities inside and outside the theoretical range of the time difference between the signals;

[0008] Classifying and statistically analyzing according to the measured time difference between the signals and the time difference range to obtain the probabilities inside and outside the actual range of the time difference between the signals;

[0009] Judging signal alignment according to the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range.

[0010] In one embodiment, the jitter distribution data includes the jitter probability density of the signal obtained by analyzing the signal link through theoretical calculation or using circuit simulation software; the obtaining the probability density of the time difference between the signals based on the jitter distribution data of each signal includes: based on the jitter probability density of each signal, calculating the probability density of the time difference between the signals using the probability density function.

[0011] In one embodiment, the jitter distribution data includes data obtained through signal jitter measurement; obtaining the probability density of the time difference between signals based on the jitter distribution data of each signal path includes: performing probability density fitting sampling on the measured data to obtain the probability density of the time difference between signals.

[0012] In one embodiment, the multiple signals include a first signal and a second signal with arbitrary jitter, and the jitter probability density of the first signal is , and the jitter probability density of the second signal is , and the probability density of the time difference between the first signal and the second signal is: , where .

[0013] In one embodiment, if both the first signal and the second signal include random jitter, the jitter probability density of the first signal is:

[0014]

[0015] where, represents the jitter probability density of the first signal with respect to time t1, is the root mean square value of the random jitter in the first signal;

[0016] The jitter probability density of the second signal is:

[0017]

[0018] where, represents the jitter probability density of the second signal with respect to time t2, is the root mean square value of the random jitter in the second signal.

[0019] In one embodiment, obtaining the probability density of the time difference between signals based on the jitter distribution data of each signal path includes:

[0020]

[0021] where, represents the probability density of the time difference between the first signal and the second signal.

[0022] In one embodiment, if the first signal includes random jitter and the second signal includes random jitter and duty cycle distortion jitter, the jitter probability density of the first signal is:

[0023]

[0024] where, represents the jitter probability density of the first signal over time t1, and is the root mean square value of the random jitter in the first signal;

[0025] The jitter probability density of the second signal is:

[0026]

[0027] where represents the jitter probability density of the second signal over time t2, and is the root mean square value of the random jitter in the second signal; is the period of the duty cycle distortion jitter in the second signal.

[0028] In one embodiment, based on the jitter distribution data of each signal, obtaining the probability density of the time difference between signals includes:

[0029]

[0030] where represents the probability density regarding the time difference between the first signal and the second signal.

[0031] In one embodiment, if the first signal includes random jitter and the second signal includes random jitter and data-dependent jitter, then the jitter probability density of the first signal is:

[0032]

[0033] where represents the jitter probability density of the first signal over time t1, and is the root mean square value of the random jitter in the first signal;

[0034] The jitter probability density of the second signal is:

[0035]

[0036] where represents the jitter probability density of the second signal over time t2, and is the root mean square value of the random jitter in the second signal; T i is the signal edge deviation introduced by any data pattern in the second signal, p i is the probability of the occurrence of this data pattern.

[0037] In one embodiment, based on the jitter distribution data of each signal, obtaining the probability density of the time difference between signals includes:

[0038]

[0039] Among them, represents the probability density of the time difference between the first signal and the second signal with respect to the time difference.

[0040] In one embodiment, by combining the probability density of the time difference and the set time difference range, the probabilities inside and outside the theoretical range of the time difference between signals are obtained, including:

[0041]

[0042] Among them, represents the probability that the time difference between the first signal and the second signal is within the time range, while represents the probability that the time difference between the first signal and the second signal is outside the time range, are the lower limit value and the upper limit value of the time difference range respectively.

[0043] In one embodiment, based on the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range, signal alignment judgment is performed, including: If the difference between the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range of the two signals is within the set error range, it is considered that the two signals are aligned.

[0044] The second aspect of the present application provides a calibration alignment judgment device, including:

[0045] A data acquisition module, configured to acquire the jitter distribution data of multiple signals to be calibrated for determining whether they are aligned;

[0046] A data analysis module, configured to obtain the probability density of the time difference between signals based on the jitter distribution data of each signal;

[0047] A probability statistics module, configured to combine the probability density of the time difference and the set time difference range to obtain the probabilities inside and outside the theoretical range of the time difference between signals; perform classification statistics based on the measured time difference between signals and the time difference range to obtain the probabilities inside and outside the actual range of the time difference between signals;

[0048] An alignment judgment module, configured to perform signal alignment judgment based on the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range.

[0049] The third aspect of the present application provides a test machine, including a resource board, and the resource board performs calibration alignment judgment according to the above method.

[0050] A fourth aspect of the present application provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0051] For the above calibration alignment judgment method, device, testing machine and electronic device, the jitter distribution data of multiple signals to be calibrated and judged for alignment is obtained. Based on the jitter distribution data of each signal, the probability density of the time difference between signals is obtained. Then, by combining the probability density of the time difference and the set time difference range, the probability inside and outside the theoretical range of the time difference between signals is obtained. Comparing the actual probability inside and outside the time difference between signals obtained by measurement and statistics with the theoretical probability inside and outside the range is used as the basis for judging whether the edge moment means of the signals are aligned. It can be applied to jitters in different situations, reduce the influence of jitter calibration alignment, and improve the calibration reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flowchart of the calibration alignment judgment method in an embodiment;

[0053] Figure 2 is a schematic diagram of the flow principle of the calibration alignment judgment method in an embodiment;

[0054] Figure 3 is a block diagram of the structure of the calibration alignment judgment device in an embodiment;

[0055] Figure 4 is an internal structure diagram of an electronic device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] In one embodiment, as Figure 1 shown, a calibration alignment judgment method is provided, including:

[0058] Step S110: Obtain the jitter distribution data of multiple signals to be calibrated and judged for alignment.

[0059] Among them, the multiple signals to be calibrated for alignment judgment are signals that have completed calibration or are in the process of calibration. For example, the multiple signals can include one signal that has completed calibration and several other signals in the process of calibration. All the multiple signals have completed calibration, or all the multiple signals are in the process of calibration. For example, when there are two signals to be calibrated for alignment judgment, one signal that has completed calibration is used to judge alignment with the other signal, and the other signal is a signal that has completed calibration or is in the process of calibration; when there are more than three signals to be calibrated for alignment judgment, one signal that has completed calibration can be used to judge alignment with other signals respectively.

[0060] This calibration alignment judgment method is applied to the FPGA program of the host computer or the resource board. The host computer can include, but is not limited to, industrial control computers, computer devices, or PCs, etc. The resource board can include, but is not limited to, power supply boards, digital boards, analog-digital hybrid boards, or time measurement boards, etc.

[0061] During the calibration and alignment process, signal jitter will affect the accuracy of signal calibration. Signal jitter refers to the deviation between the arrival time of the signal edge and the ideal time. Jitter can be divided into random jitter (Random Jitter, RJ) and deterministic jitter (Deterministic Jitter, DJ). Random jitter is caused by unpredictable noise sources, such as the thermal noise of components in the system or the influence of processing technology. Since random noise is the superposition of multiple uncorrelated noises, its characteristics can be described by a Gaussian distribution according to statistics. Compared with random jitter, deterministic jitter is a time jitter that can be repeated and predicted. Deterministic jitter can be further divided into periodic jitter (Periodic Jitter, PJ), data-dependent jitter (Data Dependent Jitter, DDJ), duty cycle distortion jitter (Duty Cycle Distortion Jitter, DCDJ), and bounded uncorrelated jitter (Bounded Uncorrelated Jitter, BUJ). The jitter of the actual signal is a combination of multiple jitter models, and multiple jitters jointly affect the accuracy of signal calibration.

[0062] Among the multiple signals to be calibrated for alignment determination, the types of jitter included may be the same or different. For example, each signal may include one or both of random jitter and deterministic jitter. The jitter distribution data of each signal can be obtained through theoretical simulation or measurement. For example, for a test machine system under design, the jitter distribution data can include the jitter probability density of the signal obtained by analyzing the signal link through theoretical calculation or using circuit simulation software for simulation. For a system that has been designed and produced, signal jitter measurement can be performed using equipment such as an oscilloscope to obtain more accurate jitter distribution data.

[0063] Step S120: Based on the jitter distribution data of each signal, obtain the probability density of the time difference between the signals.

[0064] According to the different ways of obtaining the jitter distribution data, the method of determining the probability density of the time difference will also correspondingly be different. When the jitter distribution data includes the jitter probability density of the signal obtained by analyzing the signal link through theoretical calculation or using circuit simulation software for simulation, then based on the jitter probability density of each signal, the probability density of the time difference between the signals can be calculated using its theoretical probability density function; when the jitter distribution data includes the data obtained through signal jitter measurement, then probability density fitting sampling can be performed according to the measured data to obtain the probability density of the time difference between the signals.

[0065] Specifically, taking the calculation of the probability density of the time difference using the probability density function as an example, the multiple signals include a first signal and a second signal with any jitter. The jitter probability density of the first signal is and the jitter probability density of the second signal is . The probability density of the time difference between the first signal and the second signal is: where . Among them, the first signal may include random jitter and / or duty cycle distortion jitter, and the second signal may also include random jitter and / or duty cycle distortion jitter. If the first signal includes random jitter and the second signal includes random jitter and duty cycle distortion jitter, then the jitter probability density of the first signal can be expressed using the following expression:

[0066]

[0067] where represents the jitter probability density of the first signal with respect to time t1, and is the root mean square value (RMS) of the random jitter in the first signal.

[0068] The random jitter probability density of the second signal can be expressed using the following expression:

[0069]

[0070] The duty cycle distortion jitter probability density of the second signal can be expressed by the following expression:

[0071]

[0072] Then the jitter probability density of the second signal can be expressed by the following expression:

[0073]

[0074] Wherein, represents the jitter probability density of the second signal with respect to time t2, is the root mean square value of the random jitter in the second signal; is the period of the duty cycle distortion jitter in the second signal, and the period of the duty cycle distortion jitter in the second signal is the time interval between the rising edge and the falling edge of the second signal.

[0075] It can be understood that depending on the actual situation, the jitter probability density of the first signal , and the jitter probability density of the second signal will have different specific forms. For ease of understanding, the following will take the example where the first signal includes random jitter and the second signal includes random jitter and duty cycle distortion jitter for calibration alignment judgment description.

[0076] Let the time difference between the two signals , then there is:

[0077]

[0078] The time difference has the following probability density expression:

[0079]

[0080] That is:

[0081] Integrating , the probability density expression of the time difference between the signals can be obtained:

[0082]

[0083] Wherein, represents the probability density of the time difference between the first signal and the second signal.

[0084] In one embodiment, if both the first signal and the second signal include random jitter, the jitter probability density of the first signal is:

[0085]

[0086] Among them, represents the jitter probability density of the first signal with respect to time t1, is the root mean square value of the random jitter in the first signal.

[0087] The jitter probability density of the second signal is:

[0088]

[0089] Among them, represents the jitter probability density of the second signal with respect to time t2, is the root mean square value of the random jitter in the second signal.

[0090] Specifically, based on the jitter distribution data of each signal, the probability density of the time difference between signals is obtained, including:

[0091]

[0092] Among them, represents the probability density of the time difference between the first signal and the second signal.

[0093] In another embodiment, if the first signal includes random jitter and the second signal includes random jitter and data-dependent jitter, then the jitter probability density of the first signal is:

[0094]

[0095] Among them, represents the jitter probability density of the first signal with respect to time t1, is the root mean square value of the random jitter in the first signal.

[0096] The jitter probability density of the second signal is:

[0097]

[0098] Among them, represents the jitter probability density of the second signal with respect to time t2, is the root mean square value of the random jitter in the second signal; T i is the signal edge deviation introduced by any data pattern in the second signal, p i is the probability of the occurrence of this data pattern.

[0099] Specifically, based on the jitter distribution data of each signal, the probability density of the time difference between signals is obtained, including:

[0100]

[0101] Among them, represents the probability density regarding the time difference between the first signal and the second signal of.

[0102] Step S130: Combine the probability density of the time difference and the set time difference range to obtain the probabilities inside and outside the theoretical range of the time difference between the signals. Among them, the values of the boundary values of the time difference range are not unique. As Figure 2 shown, based on the jitter distribution data of the first signal and the second signal, after calculating or simulating the probability density of the time difference between the two signals, the time difference range for signal alignment can be set according to the time difference distribution of the two signals, and the probabilities within the time range and outside the time range are calculated respectively as the probabilities inside and outside the theoretical range of the time difference. Specifically, step S130 includes:

[0103]

[0104] Among them, represents the probability that the time difference between the first signal and the second signal is within the time range, while represents the probability that the time difference between the first signal and the second signal is outside the time range, are the lower limit value and the upper limit value of the time difference range respectively.

[0105] Step S140: Classify and count according to the measured time difference between the signals and the time difference range to obtain the actual probabilities inside and outside the range of the time difference between the signals. By measuring the time difference between the two signals and according to the set time difference range for signal alignment, the time difference is classified with 1 and 0. Specifically, continue to refer to Figure 2 , measure that the time difference between the first signal and the second signal is , when , the output is 1, and when or , the output is 0. Among them, if the lower limit value of the time difference range is 0 and the upper limit value is +∞, in this case, when the arrival time of the first signal is less than the arrival time of the second signal, the output is 0, is greater than the output is 1. Count the number of 1s and 0s to determine the actual probabilities inside and outside the range of the time difference for subsequent signal alignment judgment based on the probabilities inside and outside the time range.

[0106] Step S150: Perform signal alignment judgment based on the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range. Among them, if the difference between the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range of the two signals is within the set error range, it is considered that the mean values of the edge times of the two signals are aligned. The value of the error range is also not unique and can be determined based on the performance of the system signal calibration alignment index.

[0107] Specifically, classify the time difference between the first signal and the second signal measured. After statistics, the number of 0s is , and the number of 1s is . Then when , it is considered that the two signals are aligned. Further, according to the system performance index, a certain error range can also be set. When the error range is set to , if is satisfied, it is considered that the mean values of the edge times of the two signals are aligned.

[0108] In the above calibration alignment judgment method, during the alignment calibration process, the probability distribution of signal jitter is combined to classify the time difference results, and the proportion of the number of time differences within the time difference range in the classification results is statistically calculated and compared with the probabilities inside and outside the theoretical range. Based on the probability density of jitter and the distributions of the measurement results 0 and 1 as the basis for judging whether the mean values of the edge times of the signals are aligned, the influence of jitter on high-precision calibration and alignment can be reduced, and it is applicable to jitters in different situations, improving the calibration reliability.

[0109] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0110] Based on the same inventive concept, the embodiments of the present application also provide a calibration alignment judgment device for implementing the above calibration alignment judgment method. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more of the following calibration alignment judgment device embodiments can refer to the limitations on the calibration alignment judgment method in the above text and will not be repeated here.

[0111] In one embodiment, as Figure 3 shown, a calibration alignment judgment device is provided, including: a data acquisition module 110, a data analysis module 120, a probability statistics module 130, and an alignment judgment module 140, where:

[0112] The data acquisition module 110 is configured to acquire the jitter distribution data of multiple signals to be calibrated for determining whether they are aligned.

[0113] The data analysis module 120 is configured to obtain the probability density of the time difference between signals based on the jitter distribution data of each signal path.

[0114] The probability statistics module 130 is configured to combine the probability density of the time difference and a set time difference range to obtain the probabilities inside and outside the theoretical range of the time difference between signals; perform classification statistics based on the measured time difference between signals and the time difference range to obtain the probabilities inside and outside the actual range of the time difference between signals.

[0115] The alignment judgment module 140 is configured to perform signal alignment judgment based on the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range.

[0116] In one embodiment, the jitter distribution data includes the jitter probability density of signals obtained by analyzing the signal link through theoretical calculation or using circuit simulation software; the data analysis module 120 calculates the probability density of the time difference between signals based on the jitter probability density of each signal path by using the probability density function.

[0117] In one embodiment, the jitter distribution data includes data obtained by signal jitter measurement; the data analysis module 120 performs probability density fitting sampling based on the measured data to obtain the probability density of the time difference between signals.

[0118] In one embodiment, the alignment judgment module 140 is configured to consider that two signals are aligned when the difference between the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range of the two signals is within the set error range.

[0119] Each module in the above calibration alignment judgment device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of a computer device in hardware form or be independent of it, or can be stored in the memory of an electronic device in software form so that the processor can call and execute the operations corresponding to each of the above modules.

[0120] In one embodiment, an electronic device is provided. The electronic device can be a terminal, and its internal structure diagram can be as Figure 4As shown in the figure. The electronic device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the electronic device is used to exchange information between the processor and external devices. The communication interface of the electronic device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a calibration alignment judgment method. The display unit of the electronic device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the electronic device, or an external keyboard, touchpad, or mouse, etc.

[0121] Those skilled in the art can understand that Figure 4 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0122] In one embodiment, an electronic device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps in the above method embodiments.

[0123] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0124] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, it implements the steps in the above method embodiments

[0125] In one embodiment, a testing machine is provided, including a resource board. The resource board performs calibration alignment judgment according to the above method.

[0126] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0127] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0128] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. A calibration alignment judgment method, characterized in that, Including: Obtaining the jitter distribution data of multiple signals to be calibrated for determining alignment; Based on the jitter distribution data of each signal, obtain the probability density of the time difference between signals; wherein, the multiple signals include a first signal and a second signal containing arbitrary jitter, and the jitter probability density of the first signal is , and the jitter probability density of the second signal is , and the probability density of the time difference between the first signal and the second signal is: ; Combining the probability density of the time difference and a set time difference range to obtain the probabilities inside and outside the theoretical range of the time difference between signals, including: ； Among them, represents the time difference the probability of being within the time range, represents the time difference the probability of being outside the time range, are the lower limit value and the upper limit value of the time difference range respectively; Classify and count according to the time difference between the measured signals and the time difference range to obtain the probabilities inside and outside the actual range of the time difference between the signals; wherein, the measured time difference is , count the number of times when and determine the probabilities inside and outside the actual range of the time difference; Judging the signal alignment according to the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range.

2. The method according to claim 1, wherein The jitter distribution data includes the jitter probability density of the signal obtained by analyzing the signal link through theoretical calculation or using circuit simulation software; obtaining the probability density of the time difference between signals based on the jitter distribution data of each signal path includes: calculating the probability density of the time difference between signals based on the jitter probability density of each signal path using the probability density function.

3. The method according to claim 1, wherein The jitter distribution data includes data obtained by signal jitter measurement; obtaining the probability density of the time difference between signals based on the jitter distribution data of each signal path includes: performing probability density fitting sampling according to the measured data to obtain the probability density of the time difference between signals.

4. The method according to claim 2, wherein The jitter in each signal path includes one or both of random jitter and deterministic jitter.

5. The method according to claim 4, characterized in that, If the first signal includes random jitter and the second signal includes random jitter and duty cycle distortion jitter, the jitter probability density of the first signal is: ； Among them, represents the jitter probability density of the first signal with respect to time t1, is the root mean square value of the random jitter in the first signal; The jitter probability density of the second signal is: ； wherein, represents the jitter probability density of the second signal with respect to time t2, is the root mean square value of the random jitter in the second signal; is the period of the duty cycle distortion jitter in the second signal.

6. The method according to claim 5, wherein Obtaining the probability density of the time difference between signals based on the jitter distribution data of each signal path includes: ； Among them, represents the probability density of the time difference between the first signal and the second signal with respect to.

7. The method according to claim 4, wherein The deterministic jitter includes at least one of periodic jitter, data-dependent jitter, duty cycle distortion jitter, and bounded uncorrelated jitter.

8. The method according to any one of claims 1 to 7, characterized in that, Judging the signal alignment according to the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range includes: if the difference between the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range of two signals is within the set error range, it is considered that the two signals are aligned.

9. A calibration alignment judgment device, characterized in that Including: A data acquisition module for obtaining the jitter distribution data of multiple signals to be calibrated for determining alignment; A data analysis module, configured to obtain the probability density of the time difference between signals based on the jitter distribution data of each path of signals; wherein, the multiple paths of signals include a first signal and a second signal containing arbitrary jitters, and the jitter probability density of the first signal is , and the jitter probability density of the second signal is , and the probability density of the time difference between the first signal and the second signal is: ; A probability statistics module for combining the probability density of the time difference and a set time difference range to obtain the probabilities inside and outside the theoretical range of the time difference between signals, including: ； Among them, represents the time difference the probability of being within the time range, represents the time difference the probability of being outside the time range, are the lower limit value and the upper limit value of the time difference range respectively; The probability and statistics module classifies and statistically analyzes based on the time difference between the measured signals and the time difference range to obtain the probabilities inside and outside the actual range of the time difference between the signals; wherein, the measured time difference is , and statistically analyze the number of occurrences when , and determine the probabilities inside and outside the actual range of the time difference; An alignment judgment module for judging the signal alignment according to the probabilities inside and outside the theoretical range and the probabilities inside and outside the actual range.

10. A testing machine, characterized in that, Including a resource board, and the resource board performs calibration alignment judgment according to the method described in any one of claims 1 to 8.

11. An electronic device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 8.

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