Real-time oscilloscope and symbol error rate acquisition method

Abstract

Effectively obtain the symbol error rate. [Solution] The real-time oscilloscope 10 is connected to the device under test (DUT) and has one or more ports 12 for establishing a communication channel between the oscilloscope 10 and the DUT, one or more analog-to-digital converters (ADCs) 16 for sampling the received signal from the DUT and converting it into a waveform, and one or more processors 22. The oscilloscope receives and samples an operating signal from the DUT, including the actual symbols, to create an operating waveform, resamples the operating waveform so that the sample is located at the center of each unit interval, and estimates the symbols using the communication channel characteristics evaluation, the sample at the center of each unit interval, the symbol constellation of the operating waveform, and the traceback length settings as inputs to a maximum likelihood sequence estimation (MLSE) process, and obtains the symbol error rate by comparing the estimated symbols with the actual symbols.

Description

[Technical Field]

[0001] This disclosure relates to a test and measurement device, and more particularly to the detection of symbols in transmitted symbols, and further to the reduction of errors in transmitted symbols. [Background technology]

[0002] In high-speed serial data standards such as PCIe and IEEE 802.3 Ethernet (registered trademark), PAM4 signaling has been developed as an alternative to NRZ signaling. U.S. Patent No. 11,765,002 ('002 patent), issued on September 19, 2023, derives an explicit solution for a decision feedback equalizer (DFE) tap based on an extracted single linear fit pulse response (LFPR). "Multi-linear approximate pulse response for PAM4 measurement" by Tan et al., DesignCon 2024 Proceedings TR8-14, February 22, 2024, pp. 438-453, available from https: / / fliphtml5.com / mddwo / smbr / DesignCon_2024_Compiled_Papers_Tr_8-14 / (hereinafter referred to as "Tan et al.", the contents of which are incorporated herein by reference), introduces a novel method for extracting multiple LFPRs from a pattern waveform.

[0003] Multiple LFPRs hold more information about the signal. A novel DFE structure using multiple LFPRs, called a multiple pulses based DFE (MPDFE), is described in U.S. Patent Application Publication No. 2025 / 0004014, published on January 2, 2025, which is incorporated herein by reference.

[0004] As signal speeds increase, equalizers are widely used in transmitters and receivers to improve system performance. These equalizers include continuous-time linear equalizers (CTLE), feedforward equalizers (FFE), decision feedback equalizers (DFE), and the newer multi-pulse DFE (MPDFE). Of these equalizers, DFE and MPDFE detect symbols and use the detected symbols to improve the detection of subsequent symbols. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] U.S. Patent No. 11765002 [Patent Document 2] U.S. Patent Application Publication No. 2025 / 0004014 [Non-patent literature]

[0006] [Non-Patent Document 1] “Multiple Linear Fit Pulse Responses for PAM4 Measurements,” Tan et al., DesignCon 2024 Compiled Papers TR8-14, February 22, 2024, pp 438-453, [online], Internet<https: / / fliphtml5.com / mddwo / smbr / DesignCon_2024_Compiled_Papers_Tr_8-14 / > [Overview of the project] [Problems that the invention aims to solve]

[0007] If a symbol is incorrectly detected, the incorrect symbol will be used in a feedback loop as shown in Figure 1, which can cause a burst of multiple symbol errors (called error propagation). [Means for solving the problem]

[0008] Embodiments of this invention estimate symbols using Maximum Likelihood Sequence Estimation (MLSE), sometimes also called Maximum Likelihood Sequence Detection (MLSD). As described above, the equalizer's DFE and MPDFE detect symbols and use those detected symbols to improve the detection of subsequent symbols. If a symbol is incorrectly detected, the incorrect symbol is used in the feedback loop as shown in Figure 1, and this symbol error can cause a burst of multiple symbol errors (called error propagation).

[0009] To achieve improved performance in terms of symbol error rate, the embodiments disclosed herein employ maximum likelihood sequence estimation (MLSE). MLSE provides optimal solutions for symbol estimation for signals containing noise and intersymbol interference (ISI). MLSE is free from error propagation problems such as DFE. The embodiments herein include a method for simulating MLSE on a real-time oscilloscope.

[0010] A real-time oscilloscope generally digitizes a signal in real time, capturing and displaying the signal waveform in a single, continuous acquisition (waveform data acquisition). It typically uses one or more high-speed analog-to-digital converters (ADCs) and an independent clock. An equivalent-time sampling oscilloscope differs from a real-time oscilloscope in that it acquires multiple samples over multiple periods of the signal and "assembles" a waveform that includes all parts of the signal over time.

Brief Description of the Drawings

[0011] [Figure 1] FIG. 1 shows an embodiment of a decision feedback equalizer. [Figure 2] FIG. 2 shows an embodiment of a real-time oscilloscope. [Figure 3] FIG. 3 shows an embodiment of a lattice array structure used in an embodiment of the present disclosure. [Figure 4] FIG. 4 shows an example of a 4-level pulse amplitude modulation (PAM4) waveform based on a transmission signal. [Figure 5] FIG. 5 shows an enlarged view of the waveform of FIG. 4. [Figure 6] FIG. 6 shows a linear approximation pulse response extracted from the waveform of FIG. 4, and a plurality of samples are arranged at the center of the unit interval. [Figure 7] FIG. 7 shows a linear approximation pulse response in which the sample at the center of the unit interval is around the peak of the impulse. [Figure 8] FIG. 8 shows the result of maximum likelihood sequence estimation (MLSE) of a PAM4 signal. [Figure 9] FIG. 9 shows a flowchart of an embodiment of a method for detecting symbols in a waveform using MLSD. [Figure 10] FIG. 10 shows an example of a PAM4 waveform after a channel with higher loss. [Figure 11] FIG. 11 shows the impulse response regarding the channel of FIG. 10. [Figure 12] FIG. 12 shows the result of MLSE of the PAM4 signal of FIG. 10.

Embodiments for Carrying Out the Invention

[0012] Figure 2 shows an embodiment of the real-time oscilloscope 10. The real-time oscilloscope 10 includes one or more ports 12 which are any electrical signal transmission medium. The ports 12 may include receivers, transmitters, and transceivers. Each port 12 is a channel of the test measurement device 10.

[0013] The signal is sent to a sampler track-and-hold circuit 14. The track-and-hold circuit 14 stabilizes each signal for a sufficient time to enable acquisition (waveform data acquisition) by one or more high-resolution analog-to-digital converters (ADCs) 16. The analog-to-digital converters (ADCs) 16 convert the analog signal from the track-and-hold circuit 14 into a digital signal or waveform data representing this analog signal. The one or more ADCs 16 generally consist of multiple high-speed ADCs. In some configurations, one or more ADCs 16 can sample the analog signal at a sample rate of 1 GS / s (gigasamples per second) to 100 GS / s. In other configurations, the analog-to-digital converters can sample the analog signal at a rate between 2 GS / s and 250 GS / s. The digitized signals from the ADCs 16 are stored in the acquisition memory 19. The analog-to-digital converter 108 can also be a single high-resolution analog-to-digital converter, such as a 10-bit analog-to-digital converter. In addition to the acquisition memory 18, the oscilloscope may have another memory 20. Memory 20 can store program instructions for one or more processors 22, and other data as needed.

[0014] The acquisition memory 18 and other memories 20 on the oscilloscope 10 may be implemented as a processor cache, random access memory (RAM), read-only memory (ROM), solid-state memory, hard disk drive, or other memory format. The memory serves as a medium for storing data, software products, and other instructions.

[0015] User input is received from the user interface 26 and supplied to one or more processors 22. The user interface 26 includes a keyboard, mouse, trackball, touchscreen, or other operating device that the user can use to interactively operate the GUI on the display 24. The display 116 may be a digital screen or other monitor that displays waveforms, measurement data, 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 the user interface may be a combination of a touchscreen and the other input devices described above.

[0016] One or more processors 22 may be configured to execute instructions from memory, and may perform any method or associated steps indicated by such instructions, such as receiving signals acquired from acquisition memory 18 or reconstructing the signal under test from samples generated by the ADC. In the embodiments disclosed herein, one or more processors execute an MLSE process and execute instructions for detecting symbols in a waveform.

[0017] Although the components of the test and measurement device 10 are depicted as being integrated within the oscilloscope 10, those skilled in the art will understand that any of these components may be located outside the oscilloscope 10 and coupled to the oscilloscope 10 by any conventional method (e.g., wired or wireless communication medium or mechanism). For example, in some embodiments, the display 24 may be located remotely from the oscilloscope 10.

[0018] MLSE is more robust to error propagation because it examines all possible paths and selects the best one for symbol detection. In one embodiment of MLSE, all possible paths of a symbol are tracked using a trellis structure, as shown in Figure 3. MLSE can achieve optimal symbol estimation and is free from the symbol error propagation problem. MLSE may also be implemented using the Viterbi algorithm. As shown in Figure 3, each state from time 1 to 3 has a path from each of the other states in previous time.

[0019] Estimating symbols in waveforms acquired with a real-time oscilloscope using the MLSE method requires channel characterization. Channel characterization can be obtained in various ways. For example, channels can be characterized using a VNA (Vector Network Analyzer) or a TDR (Time-Domain Reflectometer). Channel characterization can also be performed with a real-time oscilloscope using a signal with a known or detectable pattern. If the pattern is detectable, the channel characteristics will be determined after pattern detection. In this application, the process of deriving the impulse response using a PAM4 signal with a known pattern is described as channel characterization estimation. Figure 4 shows the waveform of PAM4, and Figure 5 shows an enlarged view of the same waveform.

[0020] The linear fit pulse response (LFPR) can be extracted from the PAM4 waveform, as shown in Figure 6.

[0021] Based on the extracted pulse response (LFPR), the impulse response is the extracted pulse response h pulse However, this can be derived from the relationship that it is a convolution operation between an ideal pulse and an impulse response. This convolution operation relationship can be expressed in the following matrix form. [Mathematics 1] Xh impulse =hpulse Here, X is a matrix composed of ideal pulses represented by the vector x. For example, for a pulse having 4 samples per UI (unit interval), x = [000011110000]. The method used to construct the matrix X is the same as that described by Tan et al. This method includes the processes of inverting and rotating the vector x to create the rows of X.

[0022] The extracted pulse response h pulse contains noise. Therefore, in order to solve the impulse response h impulse of Equation 1 with better numerical stability and accuracy, a regularization matrix R = σ·I is introduced. Here, σ is a small scalar value, and I is an identity matrix. The impulse response h impulse is obtained from Equation 1 as follows by the inverse convolution operation using regularization. [Equation 2] h impulse =(X T X + R) -1 X T h pulse

[0023] The impulse response calculated from Equation 2 is shown in FIG. 7. The samples at the centers of a plurality of UIs around the peak of the impulse shown in FIG. 7 are used for the estimation of the channel characteristic evaluation of the MLSE routine.

[0024] After characterizing a channel from a training waveform with a known pattern, the MLSE routine can estimate the symbols of any waveform passing through that channel, and the waveform is acquired in real time using an oscilloscope. For example, MATLAB®'s MLSE function takes the input of a waveform sample in the center of the UI, the channel characterization, the PAM4 symbol constellation, and the traceback length setting for the MLSE function, and accurately estimates all symbols. Some of the estimated symbols and actual symbols are shown in Figure 8.

[0025] Figure 9 shows a flowchart of an embodiment of a method for estimating symbols in a waveform using MLSE. This process begins in step 30 with the characterization of the communication channel. The oscilloscope can obtain this characterization from a vector network analyzer or TDR device, or the oscilloscope can be fed a training signal (i.e., a signal with a known or detectable pattern, such as a PAM4 signal). Channel characterization may need to be performed only once for a given channel. The channel characterizations are saved and reused as needed.

[0026] The characterization of the channel using an oscilloscope begins with the oscilloscope entering training mode, and then, in step 40, a training signal is transmitted to the channel. Next, in step 42, the oscilloscope samples the training signal, and then, in step 44, resamples the signal so that a predetermined number exists in each unit interval (UI). In one example, this predetermined number M is equal to 32. In step 46, the oscilloscope extracts the linear approximate pulse response (LFPR).

[0027] Once the LFPR is extracted, the oscilloscope determines the channel's impulse response in step 48. In one embodiment, the impulse response is determined by deconvolution with regularization using Equation 2, as described above. Samples at the center of multiple UIs around the peak of the impulse response capture the majority of the energy of the impulse response (step 50). These samples are estimates of the channel characterization. The number of samples to capture determines what constitutes the "majority" of the energy, which can be set by the system designer. For example, the designer may define the number of samples to use and what percentage of the peak energy level is considered the "maximum".

[0028] In step 50, in the illustrated example of channel characteristic evaluation, the number of samples is limited due to the high computational complexity of MLSE. The computational complexity of MLSE is M L Here, M is the number of symbols and L is the length of channel characteristic evaluation. Because it is an exponential function, L cannot be made too large in order to make MLSE feasible. For example, MATLAB® has a complexity of 2 20 An upper limit has been set. 20 is 4 10 Because of this, MATLAB's MLSE function cannot fully cover channels in the PAM4 signal whose impulse response exceeds 10 UI. However, this is merely one example of how the computational complexity of MLSE affects its ability to perform this task.

[0029] In step 30, once the channel characteristics are evaluated, the oscilloscope receives the operating signal and performs software clock recovery. If the channel characteristics are saved, the process of evaluating the channel characteristics includes retrieving the saved channel characteristics. The oscilloscope then samples the operating signal from the DUT in step 32, and then resamples it in step 34 so that the sample is centered in the UI. The oscilloscope then performs MLSE processing using the sample centered in the UI of the waveform, the channel characteristics, the symbol constellation, and the traceback length setting as inputs to obtain a symbol estimate as output. The symbol constellation consists of the amplitude and phase of symbols in the signal. The traceback length is a configurable parameter that refers to the number of past symbol estimates that the Viterbi algorithm should consider to find the most likely path through the grid array diagram and make a reliable symbol estimate.

[0030] In step 38, the symbol error rate is determined by comparing the estimated symbols with the actual symbols. The symbol error rate determines whether the DUT meets the design specifications. If it does not meet the specifications, the DUT design needs to be modified.

[0031] It should be noted that the MLSE approach is best suited when channel characterization is accurate and MLSE can fully cover the range of channel impulse responses.

[0032] As another numerical example, the pattern waveform after a channel with higher loss is shown in Figure 10. The impulse response of a channel with higher loss takes longer to settle down. Eight samples selected around the peak of the impulse response could not adequately represent the channel response, as shown in Figure 11.

[0033] Because channel characterization cannot be fully and accurately captured with 8 samples, the MLSE routine generates many symbol estimation errors, as shown in Figure 12. To solve this problem, in some embodiments, the oscilloscope may apply other equalizers, such as CTLE or FFE. These are beneficial in shortening the duration of the impulse response. This allows the MLSE to operate efficiently using the shortened channel characterization.

[0034] Embodiments of this invention simulate MLSE on a real-time oscilloscope. Deconvolution with regularization is used to extract the channel impulse responses used for channel characterization of the MLSE. Numerical examples demonstrate that if channel characterization is accurate to a limited length, the MLSE produces accurate symbol estimations.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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

[0039] 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.

[0040] Example 1 is a real-time oscilloscope, One or more ports connected to the device under test (DUT) to establish a communication channel between the oscilloscope and the DUT, One or more analog-to-digital converters (ADCs) for sampling the signal received from the above DUT and converting it into a waveform, One or more processors and Equipped with, The one or more processors The process involves receiving and sampling the operating signal, including the actual symbols, from the above-mentioned DUT, and creating an operating waveform. The process involves resampling the waveform during the above operation so that the sample is positioned at the center of each unit interval, The process involves using the communication channel characteristics evaluation, the sample at the center of each of the above unit intervals, the symbol constellation of the above operating waveform, and the traceback length settings as inputs to the maximum likelihood sequence estimation (MLSE) process to estimate the symbols within the above operating waveform, The process of obtaining the symbol error rate by comparing the estimated symbol with the actual symbol, and It is configured to execute a program that causes one or more of the above processors to perform the task.

[0041] Embodiment 2 is a real-time oscilloscope of Embodiment 1, wherein one or more processors are further configured to execute a program that causes one or more processors to perform a process of evaluating the characteristics of the communication channel.

[0042] Example 3 is a real-time oscilloscope of Example 2, wherein a program that causes one or more processors to perform a process of evaluating the characteristics of the communication channel includes a program that causes one or more processors to perform a process of receiving communication channel characteristic evaluation from a vector network analyzer (VNA) or a time-domain reflectometer (TDR) device.

[0043] Example 4 is a real-time oscilloscope of Example 2, wherein a program causes one or more processors to perform a process to evaluate the characteristics of the communication channel, The process of setting the above oscilloscope to training mode, A process of applying a training signal having a known pattern to the above communication channel, The process involves receiving and sampling the above training signal to create a training waveform, The above training waveform is resampled to a predetermined number of samples per unit interval, The process involves extracting a linear pulse response (LFPR) from the resampled training waveform, The process of deriving the impulse response from the above LFPR, A process to select a sample at the center of the unit interval around the peak of the impulse response described above, and to estimate the communication channel characteristics described above. This includes a program that causes one or more of the above-mentioned processors to perform this task.

[0044] Example 5 is a real-time oscilloscope of Example 4, wherein a program causes one or more processors to perform the process of deriving the impulse response, The process of constructing matrix X from an ideal pulse response vector, The process of performing a deconvolution operation on the matrix X based on the above LFPR, and This includes a program that causes one or more of the above-mentioned processors to perform this task.

[0045] Example 6 is a real-time oscilloscope of Example 5, wherein a program that causes one or more processors to perform a deconvolution operation on the matrix X based on the LFPR includes a program that causes one or more processors to perform a deconvolution operation on the matrix X using regularization.

[0046] Example 7 is a real-time oscilloscope of Example 4, wherein a program causes one or more processors to perform a process to evaluate the characteristics of the communication channel, The process of determining that the duration of the impulse response of the above communication channel is long, Before the process of deriving the above impulse response, the process of applying an equalizer that shortens the duration of the above impulse response to the above sample is performed. This includes a program that causes one or more of the above-mentioned processors to perform this task.

[0047] Example 8 is a real-time oscilloscope of Example 7, wherein a program that causes one or more processors to perform a process to determine that the duration of the impulse response of the communication channel is long includes a program that determines that the time until the impulse response settles (stabilizes) exceeds a predetermined number of unit intervals.

[0048] Example 9 is a real-time oscilloscope of Example 7, wherein a program that causes one or more processors to perform the process of applying an equalizer to the above sample includes a program that causes one or more processors to perform the process of applying either a feed-forward equalizer (FFE) or a continuous-time linear equalizer (CTLE).

[0049] Example 10 is a method, The process involves sampling the operational signal on the communication channel received from the device under test (DUT) as an operational waveform, and The above process involves resampling the operating signal so that the sample is positioned at the center of each unit interval, The communication channel characteristics evaluation, the sample at the center of each of the above unit intervals, the symbol constellation and traceback length settings of the above operating waveform are used as inputs to the maximum likelihood sequence estimation (MLSE) process to generate estimated symbols in the above operating signal, and The process involves comparing the estimated symbols with the actual symbols to determine the symbol error rate. It is equipped with.

[0050] Example 11 is the method of Example 10, further comprising a process of evaluating the characteristics of a communication channel on a real-time oscilloscope using a training signal having a known or detectable pattern in order to generate a communication channel characteristic evaluation.

[0051] Example 12 is the method of Example 11, wherein the process for evaluating the characteristics of the communication channel includes a process of receiving communication channel characteristic evaluation from a vector network analyzer (VNA) or a time-domain reflectivity measurement (TDR) device.

[0052] Example 13 is the method of Example 11, wherein the process for evaluating the characteristics of the communication channel is: The process of setting the above oscilloscope to training mode, The process involves applying the above training signal to the above communication channel, The process involves receiving and sampling the above training signal to create a training waveform, The above training waveform is resampled to a predetermined number of samples per unit interval, The process involves extracting a linear pulse response (LFPR) from the resampled training waveform, The process of deriving the impulse response from the above LFPR, A process to select a sample at the center of the unit interval around the peak of the impulse response described above, and to estimate the communication channel characteristics described above. It holds.

[0053] Example 14 is the method of Example 13, wherein the process for deriving the above impulse response is: The process of constructing matrix X from an ideal pulse response vector, The process of performing a deconvolution operation on the matrix X based on the above LFPR, and It holds.

[0054] Example 15 is the method of Example 14, wherein the process of performing an inverse convolution operation on the matrix X based on the above LFPR includes a process of performing an inverse convolution operation on the matrix X using regularization.

[0055] Example 16 is the method of Example 13, wherein the process for evaluating the characteristics of the above communication channel is: The process of determining that the duration of the impulse response of the above communication channel is long, Before the process of deriving the above impulse response, the process of applying an equalizer that shortens the duration of the above impulse response to the above sample is performed. It holds.

[0056] Example 17 is the method of Example 16, wherein the process for determining that the duration of the impulse response of the communication channel is long includes the process for determining that the time until the impulse response settles (stabilizes) exceeds a predetermined number of unit intervals.

[0057] Example 18 is the method of Example 16, wherein the process of applying the equalizer to the above sample includes the application of either a feed-forward equalizer (FFE) or a continuous-time linear equalizer (CTLE).

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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]

[0062] 10 Real-time Oscilloscopes 12. One or more ports 14 Sampler Track & Hold Circuit 16. High-resolution analog-to-digital converter (ADC) 18. Acquisition Memory 20 memory 22 One or more processors 24 displays 26 User Interface

Claims

1. It is a real-time oscilloscope, One or more ports connected to the device under test (DUT) to establish a communication channel between the oscilloscope and the DUT, One or more analog-to-digital converters (ADCs) for sampling the signal received from the above DUT and converting it into a waveform, One or more processors and Equipped with, The one or more processors The process involves receiving and sampling the operating signal, including the actual symbols, from the above DUT, and creating an operating waveform. The process involves resampling the waveform during the above operation so that the sample is positioned at the center of each unit interval, The process involves using the communication channel characteristics evaluation, the sample at the center of each of the above unit intervals, the symbol constellation of the operating waveform, and the traceback length settings as inputs to the maximum likelihood sequence estimation (MLSE) process to estimate the symbols within the operating waveform. The process of obtaining the symbol error rate by comparing the estimated symbol with the actual symbol, and A real-time oscilloscope configured to execute a program that causes one or more of the above-mentioned processors to perform the following actions.

2. One or more of the above processors further, The process of setting the above oscilloscope to training mode, A process of applying a training signal having a known pattern to the above communication channel, The process involves receiving and sampling the above training signal to create a training waveform, The above training waveform is resampled to a predetermined number of samples per unit interval, The process involves extracting a linearly approximated pulse response (LFPR) from the resampled training waveform, The process of deriving the impulse response from the above LFPR, The process involves selecting a sample located at the center of the unit interval around the peak of the impulse response, estimating the communication channel characteristics, and evaluating the characteristics of the communication channel. A real-time oscilloscope according to claim 1, configured to execute a program that causes one or more of the above-mentioned processors to perform the above.

3. A program that causes one or more of the above processors to perform the process of deriving the above impulse response is, The process of constructing matrix X from the vector of an ideal pulse response, The process of performing an inverse convolution operation on the matrix X based on the above LFPR, and The real-time oscilloscope according to claim 2, which includes a program that causes one or more of the above-mentioned processors to perform the above.

4. A real-time oscilloscope according to claim 3, wherein a program that causes one or more processors to perform a deconvolution operation on the matrix X based on the above LFPR includes a program that causes one or more processors to perform a deconvolution operation on the matrix X using regularization.

5. A program that causes one or more processors to perform the process of evaluating the characteristics of the above communication channel, The process of determining that the duration of the impulse response of the above communication channel is long, Before the process of deriving the above impulse response, the process of applying an equalizer that shortens the duration of the above impulse response to the above sample is performed. The real-time oscilloscope according to claim 2, which includes a program that causes one or more of the above-mentioned processors to perform the above.

6. A real-time oscilloscope according to claim 5, comprising a program that causes one or more processors to perform a process to determine that the duration of the impulse response of the above-mentioned communication channel is long, and further including a program that determines that the time until the impulse response settles exceeds a predetermined number of unit intervals.

7. A method for obtaining the symbol error rate, The process involves sampling the operational signal on the communication channel received from the device under test (DUT) as an operational waveform, and The process involves resampling the above operating signal so that the sample is positioned at the center of each unit interval, The communication channel characteristics evaluation, the sample at the center of each of the above unit intervals, the symbol constellation of the operating waveform, and the traceback length settings are used as inputs to the maximum likelihood sequence estimation (MLSE) process, and the estimated symbols in the operating signal are generated. The process involves comparing the estimated symbols with the actual symbols to determine the symbol error rate. A method for obtaining the symbol error rate that includes the following features.

8. The symbol error rate acquisition method according to claim 7, further comprising the process of evaluating the characteristics of the communication channel on a real-time oscilloscope using a training signal having a known or detectable pattern in order to generate the above-mentioned communication channel characteristic evaluation.

9. The process for evaluating the characteristics of the above communication channel is, The process of setting the above oscilloscope to training mode, The process involves applying the above training signal to the above communication channel, The process involves receiving and sampling the above training signal to create a training waveform, The above training waveform is resampled to a predetermined number of samples per unit interval, The process involves extracting a linearly approximated pulse response (LFPR) from the resampled training waveform, The process of deriving the impulse response from the above LFPR, A process to select a sample located at the center of the unit interval around the peak of the impulse response described above, and to estimate the communication channel characteristics described above. A method for obtaining a symbol error rate according to claim 8, comprising:

10. The process for evaluating the characteristics of the above communication channel is, The process of determining that the duration of the impulse response of the above communication channel is long, Before the process of deriving the above impulse response, the process of applying an equalizer that shortens the duration of the above impulse response to the above sample is performed. A method for obtaining a symbol error rate according to claim 9, comprising:

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