An eye diagram generation method and device based on a vector network analyzer

Abstract

The application discloses an eye diagram generation method and device based on a vector network analyzer, receives S parameters measured from a to-be-measured object, obtains an input control clicked by a user on a parameter input interface and an emphasis amplitude parameter input in the input control, calls a corresponding emphasis algorithm according to a calculation position corresponding to the input control clicked by the user, processes the S parameters by using the corresponding emphasis algorithm according to the input emphasis amplitude parameter, stores images corresponding to the S parameters and the emphasized S parameters and waits for calling, processes the emphasized S parameters by using a TDR algorithm, stores images corresponding to the emphasized S parameters and time domain response parameters and waits for calling, calls a virtual bit pattern generator, performs convolution operation on the time domain response parameters, and obtains an eye diagram, and calls corresponding image display. The corresponding emphasis algorithm is called through the calculation position of the emphasis algorithm selected by the user, and the influence of different emphasis algorithms and different emphasis amplitude parameters on the S parameters is observed.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and specifically to an eye diagram generation method and apparatus based on a vector network analyzer. Background Technology

[0002] When simulating eye diagram generation using a vector network analyzer, emphasis algorithms can be applied at different stages of eye diagram generation to obtain eye diagrams with different emphasis values ​​at different stages. In the existing application, for each case, the vector network analyzer processes the obtained S-parameters using the TDR algorithm to obtain the response of the scattering parameters, then performs time-domain processing using the emphasis algorithm, and finally uses the virtual bit pattern generator in the vector network analyzer for convolution to generate the eye diagram. In this case, the user cannot observe the impact of the emphasis algorithm on the S-parameters before and after emphasis in the intermediate process using the vector network analyzer. However, in advanced waveform analysis, emphasis helps improve waveform quality, thereby further enhancing the quality of the eye diagram. Emphasis can increase the energy of the high-frequency part of the signal while maintaining the original state of the low-frequency signal. When the signal is transmitted after emphasis, although the high-frequency signal will still be attenuated, due to the previous emphasis, the final received signal has characteristics similar to the original signal. If the characteristics of the transmission system are known before transmitting the signal, then the attenuation of the signal during transmission can be accurately compensated through pre-emphasis technology, thus ensuring that the integrity of the signal is not compromised.

[0003] Therefore, users can intuitively observe the impact of emphasis on S-parameters, including the degree of influence of emphasizing algorithms at different locations and different emphasizing amplitudes on S-parameters, which can improve the user experience. Summary of the Invention

[0004] The purpose of this invention is to provide an eye diagram generation method and apparatus based on a vector network analyzer. The method calculates the position by using an emphasis algorithm selected by the user, calls the corresponding emphasis algorithm, and observes the influence of different emphasis algorithms and different emphasis amplitude parameters on the S-parameters based on the emphasis amplitude parameters input by the user.

[0005] On the one hand, the present invention provides an eye diagram generation method based on a vector network analyzer, specifically including the following steps:

[0006] S1. Receive the S-parameters measured from the test piece, obtain the input control clicked by the user on the parameter input interface and the weighting amplitude parameter entered in the input control, wherein the input control corresponds to the calculation position of the weighting algorithm;

[0007] S2. Call the corresponding weighting algorithm according to the calculation position corresponding to the input control clicked by the user, process the S parameters according to the input weighting amplitude parameter using the corresponding weighting algorithm to obtain the weighted S parameters, store the S parameters and the image corresponding to the weighted S parameters and wait for it to be called.

[0008] S3. Process the weighted S-parameters using the TDR algorithm to obtain the time-domain response parameters of the weighted S-parameters. Store the images corresponding to the weighted S-parameters and the time-domain response parameters and wait for them to be called.

[0009] S4. Call the virtual bit pattern generator and perform convolution operation with the time domain response parameters to obtain the eye diagram;

[0010] S5. Based on the image display control clicked by the user, call the corresponding image and display it on the display interface of the vector network analyzer.

[0011] In some specific implementations, the input control includes a pre-emphasis calculation input control, a first-cycle-after-emphasis input control, and a second-cycle-after-emphasis input control.

[0012] In some specific implementation schemes, the processing procedure for each emphasis algorithm is as follows:

[0013] Get the emphasis value parameter entered by the user in the input control;

[0014] Determine the degree of emphasis represented by the input emphasis amplitude parameter, which includes positive and negative values;

[0015] The S-parameters are processed by calling the corresponding weighting algorithm based on the degree of weighting.

[0016] In some specific implementations, when the input control clicked by the user is a pre-emphasis calculation input control, the first emphasis algorithm is invoked, and when the emphasis level is positive, the first emphasis algorithm invoked is as follows:

[0017] S21*=h1*Data_F.*(cos(w*t0)+1i*sin(w*t0)) + h2*Data_F

[0018] h1=(bb / a) / 2;

[0019] h2 = (b + b / a) / 2;

[0020] Where S21* represents the weighted S-parameters, Data_F represents the S-parameters measured from the device under test, w represents the frequency, t0 represents the duration of one cycle, a represents the input weighted amplitude parameter, b represents the maximum amplitude value of the T-parameters generated by the LPS step response of the S-parameters, li represents a complex number, i represents the imaginary unit, and 1 represents the imaginary part.

[0021] In some specific implementations, when the severity level is negative, the first severity algorithm invoked is:

[0022] S21*=-h1*Data_F.*(cos(w*t0)+1i*sin(w*t0)) + h2*Data_F.

[0023] In some specific implementations, when the user clicks an input control that is emphasized after the first cycle, a second emphasis algorithm is invoked. When the emphasis level is positive, the second emphasis algorithm invoked is as follows:

[0024] S21*=h2*Data_F+h1*Data_F.*(cos(w*t0)-1i*sin(w*t0))

[0025] h1=(bb / a) / 2;

[0026] h2 = (b + b / a) / 2;

[0027] Where S21* represents the weighted S-parameters, Data_F represents the S-parameters measured from the device under test, w represents the frequency, t0 represents the duration of one cycle, a represents the input weighted amplitude parameter, b represents the maximum amplitude value of the T-parameters generated by the LPS step response of the S-parameters, li represents a complex number, i represents the imaginary unit, and 1 represents the imaginary part.

[0028] In some specific implementations, when the severity level is negative, the second severity algorithm invoked is:

[0029] S21*=h1*Data_F-h2*Data_F.*(cos(w*t0)+1i*sin(w*t0)).

[0030] In some specific implementations, when the user clicks an input control that is emphasized after the second cycle, a third emphasis algorithm is invoked. When the emphasis level is positive, the third emphasis algorithm invoked is as follows:

[0031] S21*= h2*Data_F+h1*Data_F.*(cos(w*2*t0)-1i*sin(w*2*t0))

[0032] h1=(bb / a) / 2;

[0033] h2 = (b + b / a) / 2;

[0034] Where S21* represents the weighted S-parameters, Data_F represents the S-parameters measured from the device under test, w represents the frequency, t0 represents the duration of one cycle, a represents the input weighted amplitude parameter, b represents the maximum amplitude value of the T-parameters generated by the LPS step response of the S-parameters, li represents a complex number, i represents the imaginary unit, and 1 represents the imaginary part.

[0035] In some specific implementations, when the severity level is negative, the third severity algorithm invoked is:

[0036] S21*= h2*Data_F-h1*Data_F.*(cos(w*2*t0)-1i*sin(w*2*t0)).

[0037] Secondly, this application provides an eye diagram generation device based on a vector network analyzer, comprising:

[0038] Memory:

[0039] One or more processors: and

[0040] One or more modules, stored in memory and configured to be executed by the one or more processors, the one or more modules comprising:

[0041] A module that receives S-parameters measured from the test piece, obtains the input controls clicked by the user on the parameter input interface and the weighting amplitude parameters entered in the input controls, with the input controls corresponding to the calculation positions of the weighting algorithm;

[0042] The module calls the corresponding emphasis algorithm based on the calculation position corresponding to the input control clicked by the user, processes the S-parameters using the emphasis algorithm based on the input emphasis amplitude parameter, and obtains the emphasized S-parameters.

[0043] A module that processes the weighted S-parameters using the TDR algorithm to obtain the time-domain response parameters of the weighted S-parameters;

[0044] This module calls the virtual bit pattern generator and performs convolution operations with the time-domain response parameters to obtain the eye diagram.

[0045] The inventive concept of this application is as follows:

[0046] Existing emphasis processing algorithms first perform TDR processing on the S-parameters acquired from the device under test (DUT) to obtain the time-domain response of the scattering parameters. Then, they apply an emphasis algorithm to the time-domain response to obtain new scattering parameters. Finally, they use a virtual bit pattern generator to perform convolution with the new scattering parameters to generate an eye diagram. In this process, since the S-parameters are used as a transfer function, they remain unchanged throughout the TDR processing, making it impossible to observe the effect of the emphasis algorithm on the S-parameters. Furthermore, this approach also fails to observe the impact of different emphasis amplitudes on the S-parameters.

[0047] To address this, this application modifies the eye diagram emphasis process to provide users with an opportunity to observe the impact of the emphasis algorithm on the S-parameters before and after emphasis. The S-parameters are first emphasized in the frequency domain. Furthermore, to observe the influence of different amplitude parameters and emphasis algorithms at different positions on the S-parameter emphasis, different emphasis algorithms are applied to each position and amplitude parameter. An observation window is set up to store the images of the S-parameters before and after emphasis for later retrieval. Thus, when a user needs to observe the impact of the emphasis algorithm on the S-parameters at the current position and with the currently input amplitude parameter, they can click the display control to display the corresponding images of the S-parameters before and after emphasis. This helps users directly observe the impact of different amplitude parameters and emphasis positions on the S-parameters, facilitating a better understanding of the emphasis algorithm's processing logic. Attached Figure Description

[0048] Figure 1 This is a flowchart of a vector mesh eye diagram display method based on an emphasis algorithm provided in an embodiment of the present invention;

[0049] Figure 2 The vector network analyzer operating interface provided in this embodiment of the invention;

[0050] Figure 3 This is a schematic diagram of the eye diagram generated by the pre-emphasis algorithm provided in this embodiment of the invention;

[0051] Figure 4 This is a schematic diagram of the eye diagram generated by the post-cycle emphasis algorithm provided in an embodiment of the present invention;

[0052] Figure 5 This is a schematic diagram of the eye diagram generated by the second-cycle-after-emphasis algorithm provided in an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0055] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0056] Furthermore, for clarity and brevity, descriptions of well-known structures, functions, and configurations may have been omitted. Those skilled in the art will recognize that various changes and modifications can be made to the examples described herein without departing from the spirit and scope of this disclosure.

[0057] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0058] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0059] Existing methods for generating eye diagrams based on vector network analyzers generally treat the transfer function in the S-parameters of the system under test as a transfer function (i.e., the S12 direct scattering parameter input). Then, using a time-domain reflectometer / time-domain transfer meter (TDR / TDT) algorithm, the S12 parameters are converted into frequency-domain S-parameters through Fourier transform to obtain the time-domain response T12 of the S12 scattering parameters. Here, an S12 observation window can be set. However, due to the TDR / TDT algorithm used, the S-parameters are equivalent to the transfer function. That is to say, the actual steps involved in the calculation are: the S12 parameters are transformed into T12 parameters through TDR calculation, and the T12 parameters are used for convolution operation by the weighting and generator to obtain the eye diagram. The order of transformation from S12 to T12 cannot be changed, but the emphasis is applied to the T parameter. That is, the calculation order is S12 to T12, then T12 is emphasized to obtain T12 with emphasis. At this point, the S parameter remains unchanged; from the observation window, the effect of the emphasis algorithm on the S parameter is not visible. Next, time-domain processing of T12 using the emphasis algorithm yields the new T21*, with the emphasis formula as follows:

[0060] T21*=T (t + cursors level)+T

[0061] Where T represents the time-domain response T12 parameter, and t represents time, a T12 observation window can be set here to observe the changes in T12 before and after emphasis, clearly showing the impact of the emphasis algorithm on the T parameter; finally, an eye diagram is generated by convolving the virtual bit pattern generator with the new T21*. It can be seen that when using this algorithm, the impact of the emphasis algorithm on the S parameter cannot be directly observed in the set S12 and T12 observation windows. Therefore, to provide users with a better auxiliary observation window and help them better understand the impact of the emphasis algorithm on the S parameter, this application proposes different emphasis algorithms for each emphasis position and different emphasis levels, so that the impact of the emphasis algorithm on the S parameter before and after emphasis can be observed during the eye diagram generation process. The specific implementation is as follows:

[0062] Example 1

[0063] like Figure 1 As shown, this embodiment provides an eye diagram generation method based on a vector network analyzer, specifically including the following steps:

[0064] S1. Receive the S-parameters measured from the test piece, obtain the input control clicked by the user on the parameter input interface and the weighting amplitude parameter entered in the input control, wherein the input control corresponds to the calculation position of the weighting algorithm;

[0065] The input controls include a pre-emphasis calculation input control, an emphasis input control after the first cycle, and an emphasis input control after the second cycle.

[0066] S2. Call the corresponding weighting algorithm according to the calculation position corresponding to the input control clicked by the user, process the S parameters according to the input weighting amplitude parameter using the corresponding weighting algorithm to obtain the weighted S parameters, store the S parameters and the image corresponding to the weighted S parameters and wait for it to be called.

[0067] For each emphasis algorithm, the processing procedure is as follows:

[0068] Get the emphasis value parameter entered by the user in the input control;

[0069] Determine the degree of emphasis represented by the input emphasis amplitude parameter, which includes positive and negative values;

[0070] The S-parameters are processed by calling the corresponding weighting algorithm based on the degree of weighting.

[0071] This embodiment provides a specific method for applying emphasis at three different locations:

[0072] 1. Pre-intensification (intensifying the treatment one cycle in advance)

[0073] When the user clicks an input control that is a pre-emphasis calculation input control, the first emphasis algorithm is invoked. When the emphasis level is positive (i.e., 'a' is greater than 0), the first emphasis algorithm invoked is as follows:

[0074] S21*=h1*Data_F.*(cos(w*t0)+1i*sin(w*t0)) + h2*Data_F

[0075] h1=(bb / a) / 2;

[0076] h2 = (b + b / a) / 2;

[0077] Where S21* represents the weighted S-parameters, Data_F represents the S-parameters measured from the device under test, w represents the frequency, t0 represents the duration of one cycle, and the duration of one cycle is equal to the reciprocal of the data rate t0=1 / data_rate; a represents the input weighted amplitude parameter, b represents the maximum amplitude value of the T-parameters generated by the LPS step response of the S-parameters, li represents a complex number, i represents the imaginary unit, and 1 represents the imaginary part; .* represents dot product calculation;

[0078] The process of obtaining b is as follows: The vector network analyzer is connected to the DUT and generates different S12 parameters for different DUTs. After passing through the TDR, the time domain response is T12. For the T12 generated by the LPS step response, the maximum value is taken as b.

[0079] When the severity level is negative (i.e., a is less than 0), the first severity algorithm invoked is:

[0080] S21*=-h1*Data_F.*(cos(w*t0)+1i*sin(w*t0)) + h2*Data_F.

[0081] 2. Worsening after the first cycle

[0082] When the user clicks an input control that is emphasized after the first cycle, the second emphasis algorithm is invoked. When the emphasis level is positive (i.e., 'a' is greater than 0), the second emphasis algorithm invoked is as follows:

[0083] S21*=h2*Data_F+h1*Data_F.*(cos(w*t0)-1i*sin(w*t0))

[0084] When the severity level is negative (i.e., a is less than 0), the second severity algorithm is invoked as follows:

[0085] S21*=h1*Data_F-h2*Data_F.*(cos(w*t0)+1i*sin(w*t0)).

[0086] 3. Worsening after the second cycle

[0087] When the user clicks an input control that is emphasized after the second cycle, the third emphasis algorithm is invoked. When the emphasis level is positive (i.e., 'a' is greater than 0), the third emphasis algorithm invoked is as follows:

[0088] S21*= h2*Data_F+h1*Data_F.*(cos(w*2*t0)-1i*sin(w*2*t0))

[0089] When the severity level is negative (i.e., a < 0), the third severity algorithm is invoked as follows:

[0090] S21*= h2*Data_F-h1*Data_F.*(cos(w*2*t0)-1i*sin(w*2*t0)).

[0091] S3. Process the weighted S-parameters using the TDR algorithm to obtain the time-domain response parameters of the weighted S-parameters. Store the images corresponding to the weighted S-parameters and the time-domain response parameters and wait for them to be called.

[0092] S4. Call the virtual bit pattern generator and perform convolution operation with the time domain response parameters to obtain the eye diagram;

[0093] S5. Based on the image display control clicked by the user, call the corresponding image and display it on the display interface of the vector network analyzer.

[0094] To better illustrate this embodiment, the vector network analyzer's operating interface includes a display interface and an eye diagram generation scene control (control name: Advanced Waveform). During operation, the user first connects the vector network analyzer to the device under test (DUT) or pre-sets the S-parameters for eye diagram generation. Clicking the eye diagram generation scene control then redirects to the eye diagram generation control panel. The control panel displays the calculation flow of the emphasis algorithm, including the receiver (Rx), the transmitter (Tx), a control for triggering the jitter algorithm configuration (control name: Jitter), a configuration trigger control for the emphasis algorithm configuration (control name: Emphasis), and a DUT control. It can be seen that an observation window (View) is provided between the emphasis algorithm and the DUT. When the user clicks the Emphasis configuration trigger control, the parameter input interface is invoked; for example... Figure 2 As shown, the parameter input interface includes a running control (control name: Emphasis Enable) to trigger the emphasis algorithm, an input control (control name: cursorslevel), and a control to display the intermediate emphasis processing steps (control name: showTDR / TDT). The input controls include a pre-emphasis calculation input control (control name: Pre Cursor), an emphasis input control after the first cycle (control name: Post1 Cursor), and an emphasis input control after the second cycle (control name: Post2 Cursor).

[0095] The user selects the calculation location of the desired emphasis algorithm, clicks the corresponding input control to bring up the virtual input keyboard, and enters the desired emphasis amplitude parameters in the input box of the parameter input control. The user then clicks the Emphasis Enable control, and the software background calls the corresponding emphasis algorithm to perform frequency domain emphasis processing on the S-parameters.

[0096] If the current data rate of the vector network analyzer is set to 5Gb / s, and a 2.4mm coaxial cable is used to connect to the device under test, the S-parameters here are the S12 direct scattering parameter inputs. The S12 direct scattering parameter inputs are the transfer function in the S-parameters of the device under test or system, which is regarded as the transfer function. During the frequency domain emphasis process, the corresponding emphasis algorithm is first called to perform frequency domain processing on S12 to obtain the new S21*.

[0097] An S12 observation window is set up at this location. This window plots the data before and after S12 emphasis processing into a comparison image of the S-parameters before and after emphasis processing, and stores it in a register, awaiting retrieval. It can be seen that since this application first performs frequency domain processing on S12 using the emphasis algorithm, the comparison of the currently selected emphasis algorithm before and after S12 emphasis can be seen in the S12 observation window, allowing analysis of the impact of the current emphasis level on the S-parameters. Next, the new S21* is processed using the TDR / TDT algorithm to obtain the time-domain response T12* of the S21* scattering parameters. Another T12 observation window can be set up at this location to plot the data before and after T12 emphasis processing into a comparison image of the scattering parameters before and after emphasis processing, and store it in a register, awaiting retrieval. To achieve the final eye diagram display, a virtual bit pattern generator is used to perform convolution calculations with T12* to generate eye diagram data. The eye diagram data is then plotted as an eye diagram image and stored in a register, awaiting retrieval.

[0098] After the user clicks the run control, the vector network analyzer has already stored multiple images to be displayed, such as... Figure 3 As shown, if the user clicks the trigger control for the intermediate process of the weighting process, they can see the comparison images of S-parameters before and after weighting and the comparison images of scattering parameters before and after weighting on the display interface of the vector network analyzer. If the user clicks the eye diagram generation control, they can see the generated eye diagram image on the display interface of the vector network analyzer.

[0099] For example, when the user selects the pre-emphasis calculation input control, and the input emphasis amplitude parameter 'a' is 3dB, the eye diagram generated by the algorithm corresponding to the positive value in the first emphasis algorithm is as follows: Figure 3 As shown, when the user selects the first cycle of the emphasis input control and inputs an emphasis amplitude parameter 'a' of -3.5dB, the eye diagram generated by the algorithm corresponding to the negative value in the second emphasis algorithm is as follows. Figure 4 As shown; when the user selects the second-cycle emphasis input control and the input emphasis amplitude parameter 'a' is 3dB, the eye diagram generated by the algorithm corresponding to the negative value in the second emphasis algorithm is as follows. Figure 5 As shown.

[0100] Example 2

[0101] This embodiment provides an eye diagram generation device based on a vector network analyzer, including:

[0102] Memory:

[0103] One or more processors: and

[0104] One or more modules, stored in memory and configured to be executed by the one or more processors, the one or more modules comprising:

[0105] A module that receives S-parameters measured from the test piece, obtains the input controls clicked by the user on the parameter input interface and the weighting amplitude parameters entered in the input controls, with the input controls corresponding to the calculation positions of the weighting algorithm;

[0106] The module calls the corresponding emphasis algorithm based on the calculation position corresponding to the input control clicked by the user, processes the S-parameters using the emphasis algorithm based on the input emphasis amplitude parameter, and obtains the emphasized S-parameters.

[0107] A module that processes the weighted S-parameters using the TDR algorithm to obtain the time-domain response parameters of the weighted S-parameters;

[0108] This module calls the virtual bit pattern generator and performs convolution operations with the time-domain response parameters to obtain the eye diagram.

[0109] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Based on the technical essence of the present invention, any simple modifications, equivalent substitutions, and improvements made to the above embodiments within the spirit and principles of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for generating eye diagrams based on a vector network analyzer, characterized in that, Specifically, the following steps are included: S1. Receive the S-parameters measured from the test piece, obtain the input control clicked by the user on the parameter input interface and the weighting amplitude parameter entered in the input control, wherein the input control corresponds to the calculation position of the weighting algorithm; The input controls include a pre-emphasis calculation input control, an emphasis input control after the first cycle, and an emphasis input control after the second cycle. S2. Call the corresponding weighting algorithm according to the calculation position corresponding to the input control clicked by the user, process the S parameters according to the input weighting amplitude parameter using the corresponding weighting algorithm to obtain the weighted S parameters, store the S parameters and the image corresponding to the weighted S parameters and wait for it to be called. S3. Process the weighted S-parameters using the TDR algorithm to obtain the time-domain response parameters of the weighted S-parameters. Store the images corresponding to the weighted S-parameters and the time-domain response parameters and wait for them to be called. S4. Call the virtual bit pattern generator and perform convolution operation with the time domain response parameters to obtain the eye diagram; S5. Based on the image display control clicked by the user, call the corresponding image and display it on the display interface of the vector network analyzer.

2. The eye diagram generation method based on a vector network analyzer according to claim 1, characterized in that, For each emphasis algorithm, the processing procedure is as follows: Get the emphasis value parameter entered by the user in the input control; Determine the degree of emphasis represented by the input emphasis amplitude parameter, which includes positive and negative values; The S-parameters are processed by calling the corresponding weighting algorithm based on the degree of weighting.

3. The eye diagram generation method based on a vector network analyzer according to claim 2, characterized in that, When the user clicks an input control that is a pre-emphasis calculation input control, the first emphasis algorithm is invoked. Furthermore, when the emphasis level is positive, the first emphasis algorithm invoked is as follows: S21*=h1*Data_F.*(cos(w*t0)+1i*sin(w*t0)) + h2*Data_F h1=(bb / a) / 2; h2 = (b + b / a) / 2; Where S21* represents the weighted S-parameters, Data_F represents the S-parameters measured from the device under test, w represents the frequency, t0 represents the duration of one cycle, a represents the input weighted amplitude parameter, b represents the maximum amplitude value of the T-parameters generated by the LPS step response of the S-parameters, li represents a complex number, i represents the imaginary unit, 1 represents the imaginary part, and .* represents the dot product calculation.

4. The eye diagram generation method based on a vector network analyzer according to claim 3, characterized in that, When the severity level is negative, the first severity algorithm invoked is: S21*=-h1*Data_F.*(cos(w*t0)+1i*sin(w*t0)) + h2*Data_F.

5. The eye diagram generation method based on a vector network analyzer according to claim 2, characterized in that, When the user clicks an input control that is emphasized after the first cycle, the second emphasis algorithm is invoked. When the emphasis level is positive, the second emphasis algorithm invoked is as follows: S21*=h2*Data_F+h1*Data_F.*(cos(w*t0)-1i*sin(w*t0)) h1=(bb / a) / 2; h2 = (b + b / a) / 2; Where S21* represents the weighted S-parameters, Data_F represents the S-parameters measured from the device under test, w represents the frequency, t0 represents the duration of one cycle, a represents the input weighted amplitude parameter, b represents the maximum amplitude value of the T-parameters generated by the LPS step response of the S-parameters, li represents a complex number, i represents the imaginary unit, 1 represents the imaginary part, and .* represents the dot product calculation.

6. The eye diagram generation method based on a vector network analyzer according to claim 5, characterized in that, When the severity level is negative, the second severity algorithm is invoked as follows: S21*=h1*Data_F-h2*Data_F.*(cos(w*t0)+1i*sin(w*t0)).

7. The eye diagram generation method based on a vector network analyzer according to claim 2, characterized in that, When the user clicks an input control that is emphasized after the second cycle, the third emphasis algorithm is invoked. When the emphasis level is positive, the third emphasis algorithm invoked is as follows: S21*= h2*Data_F+h1*Data_F.*(cos(w*2*t0)-1i*sin(w*2*t0)) h1=(bb / a) / 2; h2 = (b + b / a) / 2; Where S21* represents the weighted S-parameters, Data_F represents the S-parameters measured from the device under test, w represents the frequency, t0 represents the duration of one cycle, a represents the input weighted amplitude parameter, b represents the maximum amplitude value of the T-parameters generated by the LPS step response of the S-parameters, li represents a complex number, i represents the imaginary unit, 1 represents the imaginary part, and .* represents the dot product calculation.

8. The eye diagram generation method based on a vector network analyzer according to claim 7, characterized in that, When the severity level is negative, the third severity algorithm is invoked as follows: S21*=h2*Data_F-h1*Data_F.*(cos(w*2*t0)-1i*sin(w*2*t0)).

9. An eye diagram generation device based on a vector network analyzer, characterized in that, include: Memory: One or more processors: and One or more modules, stored in memory and configured to be executed by the one or more processors, the one or more modules comprising: A module that receives S-parameters measured from the test piece, obtains the input controls clicked by the user on the parameter input interface and the weighting amplitude parameters entered in the input controls, with the input controls corresponding to the calculation positions of the weighting algorithm; The input controls include a pre-emphasis calculation input control, an emphasis input control after the first cycle, and an emphasis input control after the second cycle. The module calls the corresponding emphasis algorithm based on the calculation position corresponding to the input control clicked by the user, processes the S-parameters using the emphasis algorithm based on the input emphasis amplitude parameter, and obtains the emphasized S-parameters. A module that processes the weighted S-parameters using the TDR algorithm to obtain the time-domain response parameters of the weighted S-parameters; This module calls the virtual bit pattern generator and performs convolution operations with the time-domain response parameters to obtain the eye diagram.

Images