Statistical eye pattern generation method and device, and storage medium

Abstract

The invention discloses a statistical eye pattern generation method and device and a storage medium. The method comprises the following steps: inputting a step signal at a transmitting end of a channel, and determining a step response function based on the step signal; introducing a dithering signal at the transmitting end, and determining a dithering probability model based on the dithering signal; based on a jitter probability model, determining a jitter value set and a jitter probability set; determining a state transition probability matrix between the first moment and the second moment in the input step signal; based on the state transition probability matrix and the step response function, establishing an equivalent response distribution kernel for the target sampling moment; performing convolution operation on the voltage probability distribution at the previous sampling moment of the target sampling moment and the equivalent response distribution kernel, determining the voltage probability distribution at the target sampling moment, and determining the voltage probability distribution at multiple sampling moments; and generating a statistical eye pattern based on the voltage probability distribution at the plurality of sampling moments. The method has the technical effect of assisting in improving the credibility of simulation evaluation.

Description

Technical Field

[0001] This disclosure relates to the field of electronic design automation simulation technology, and in particular to a method, apparatus, and storage medium for generating statistical eye diagrams. Background Technology

[0002] As integrated circuit data communication rates reach the gigabit-per-second (Gb / s) level, signal integrity of on-chip and off-chip links has become critical to the success or failure of integrated circuit design. Many interconnect and device coupling effects that are negligible at low speeds must be included in modeling and evaluation under high-speed conditions. Furthermore, system-level behaviors, such as jitter amplification and cancellation, have evolved into typical signal integrity issues. Current automated electronic design simulation processes do not adequately consider jitter amplification caused by channels, often simply equating transmitter jitter with receiver jitter, which can easily lead to serious misjudgments. This is because transmitter jitter is "colored" and amplified by the step response under channel influence; ignoring this effect will result in overly optimistic signal integrity assessments. Therefore, improving the reliability of simulation evaluations deserves attention. Summary of the Invention

[0003] In view of this, embodiments of this application provide a method, apparatus, and storage medium for generating statistical eye diagrams, in order to help improve the credibility of simulation evaluation. Firstly, a method for generating a statistical eye diagram is provided, comprising: inputting a step signal at the transmitting end of a channel, and determining a step response function based on the step signal; introducing a jitter signal at the transmitting end, and determining a jitter probability model based on the jitter signal, the jitter probability model including a continuous probability density function or a discrete probability distribution; determining a jitter value set and a jitter probability set based on the jitter probability model, wherein the jitter values ​​in the jitter value set correspond to the jitter probabilities in the jitter probability set; at a first time and a second time in the input step signal, determining a state transition probability matrix between the first time and the second time based on the jitter value set and the jitter probability set, wherein the first time and the second time are separated by one bit period; establishing an equivalent response distribution kernel for the target sampling time based on the state transition probability matrix and the step response function; performing a convolution operation between the voltage probability distribution of the previous sampling time of the target sampling time and the equivalent response distribution kernel to determine the voltage probability distribution of the target sampling time, and repeating the convolution operation at consecutive sampling times to determine the voltage probability distribution of multiple sampling times; and generating a statistical eye diagram based on the voltage probability distribution of multiple sampling times.

[0004] Optionally, when the jitter probability model is a continuous probability density function, the jitter signal is divided into multiple first jitter value subsets based on a preset jitter value segmentation; the median of the multiple first jitter values ​​in the first jitter value subset is taken as the second jitter value of the jitter value subset; and the second jitter probability of the second jitter value is determined based on the first jitter probability of the multiple first jitter values.

[0005] Optionally, the matrix elements of the state transition probability matrix include the joint probability of the second jitter probabilities among multiple second jitter values.

[0006] Optionally, the step signal is a single-edge step signal, and the step response function is a single-edge step response function.

[0007] Optionally, generating a statistical eye diagram based on the voltage probability distribution at multiple sampling times includes: folding multiple sampling times onto the same unit interval phase axis according to the bit period; superimposing the voltage probability distributions of the same or adjacent phases to obtain a first voltage-phase two-dimensional probability distribution, where the phase is the relative position of the sampling time with respect to the bit period; normalizing the first voltage-phase two-dimensional probability distribution to determine a second voltage-phase two-dimensional probability distribution; and mapping the second voltage-phase two-dimensional probability distribution to a preset color to generate a statistical eye diagram.

[0008] Optionally, the voltage probability distributions of the same or adjacent phases are superimposed and calculated, including: summing or averaging the voltage probability distributions from different bit periods within the same phase unit; and smoothing the voltage probability distributions of adjacent phase units according to preset weights.

[0009] Optionally, an equivalent response distribution kernel is established for the target sampling time, including: time-shifting the step response function based on the jitter value set to form multiple shifted responses; weighting and superimposing the shifted responses according to the matrix elements of the state transition probability matrix to obtain an equivalent response distribution kernel for the target sampling time, wherein the equivalent response distribution kernel is used to characterize the influence of the channel on the jitter of the transmitter, and the weighted superposition is used to characterize the amplification effect or colorization effect of the channel on the jitter of the transmitter.

[0010] Optionally, the first moment corresponds to the target sampling phase within the current bit period; the second moment corresponds to the corresponding sampling phase that is one bit period apart from the target sampling phase.

[0011] Secondly, a statistical eye diagram generation apparatus is provided, comprising: a step signal input unit for inputting a step signal at the transmitting end of a channel and determining a step response function based on the step signal; a jitter introduction unit for introducing a jitter signal at the transmitting end and determining a jitter probability model based on the jitter signal, the jitter probability model including a continuous probability density function or a discrete probability distribution; and a determination unit for determining a jitter value set and a jitter probability set based on the jitter probability model, wherein the jitter values ​​in the jitter value set correspond to the jitter probabilities in the jitter probability set, and for determining the jitter value set based on the jitter probability model at a first time and a second time in the input step signal. The system consists of a set of jitter probabilities and a state transition probability matrix between the first and second time moments, where the first and second time moments are separated by one bit period. A convolution unit is used to establish an equivalent response distribution kernel for the target sampling moment based on the state transition probability matrix and the step response function, and to perform convolution operations between the voltage probability distribution of the previous sampling moment and the equivalent response distribution kernel to determine the voltage probability distribution of the target sampling moment. This convolution operation is repeated at consecutive sampling moments to determine the voltage probability distribution of multiple sampling moments. A generation unit is used to generate a statistical eye diagram based on the voltage probability distribution of multiple sampling moments.

[0012] Thirdly, a computer-readable storage medium is provided, comprising: a memory having instructions stored thereon; and a processor configured to implement the statistical eye diagram generation method provided in the first aspect when reading the memory. Attached Figure Description

[0013] The accompanying drawings used in the description of the embodiments of this disclosure are briefly introduced below: Figure 1 The diagram shows a flowchart of a method for generating a statistical eye diagram according to some embodiments of this application; Figure 2 A schematic diagram of the structure of a statistical eye diagram generation apparatus provided in some embodiments of this application is shown. Detailed Implementation

[0014] To more clearly illustrate the technical solutions in the embodiments of this disclosure, examples of implementation methods of this disclosure will be described below with reference to the accompanying drawings. The accompanying drawings described below are merely some embodiments of this disclosure. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without creative effort. Adjustments and improvements made without departing from the concept of this disclosure are all within the protection scope of this disclosure.

[0015] To keep the drawings simple, each figure only schematically shows the parts relevant to the embodiment, and they do not represent the actual structure of the product. In addition, for the sake of clarity and ease of understanding, some figures only schematically show parts of components with the same structure or function, and there may actually be more or fewer components with the same structure or function.

[0016] In this disclosure, unless otherwise expressly specified and limited, ordinal numbers, such as “first”, “second”, etc., are used only to distinguish and describe related objects, and should not be construed as indicating or implying the relative importance or order between related objects; furthermore, they do not represent the quantity of related objects. “Multiple” includes two or more, and other quantifiers are similar. “ / ” is used to describe the relationship between related objects, indicating an “or” relationship between them. “And / or” is used to describe the relationship between related objects, including any combination relationship between them, such as “a and / or b” including: “a alone”, “b alone”, or “a and b”. “One or more” or “at least one” of multiple objects refers to any object or any combination of multiple objects, such as “one or more of a1, a2, a3” or “at least one of a1, a2, a3” including: “a1 alone”, “a2 alone”, “a3 alone”, “a1 and a2”, “a1 and a3”, “a2 and a3”, or “a1, a2 and a3”.

[0017] High-speed interconnect design is transitioning from low Gb / s to multi-Gb / s and above. In this context, physical effects such as dispersion, loss, crosstalk, and electromagnetic coupling between devices in wire interconnects directly reshape edge morphology and inter-symbol interference. Details that were negligible in low-speed scenarios now must be addressed in modeling and verification. Simultaneously, system-level behaviors, such as the amplification, cancellation, and redistribution of jitter from different sources in the channel and receiver links, significantly alter bit error rate and margin assessments, becoming an indispensable part of signal integrity analysis. However, current electronic design automation (EDA) simulations do not adequately consider the amplification effect of transmitter jitter caused by the channel. Some systems directly treat transmitter jitter as receiver jitter or add a noise model decoupled from the channel to the output. This approach easily introduces systemic biases. For example, time disturbances at the transmitter are colored by the channel's time response and converted into equivalent noise in the amplitude domain as they pass through the channel. The coupling between amplitude and phase leads to an overestimation of eye diagram opening and bit error probability estimates, resulting in overly optimistic integrated circuit link design conclusions. Therefore, this application incorporates the amplification effect of the channel on the transmitter disturbance into the simulation calculation, so that the statistical eye diagram obtained from the simulation can truly reflect the risks brought about by the channel amplification effect, thereby providing a more reliable basis for the simulation and verification of electronic automation design for high-speed transmission applications.

[0018] The following description is in conjunction with the accompanying drawings: Figure 1 The diagram illustrates a flowchart of a method for generating statistical eye diagrams according to some embodiments of this application. The method includes at least the following steps: S110: Input a step signal at the transmitting end of the channel and determine the step response function based on the step signal; S120: Introduce jitter signals at the transmitting end and determine the jitter probability model based on the jitter signals. The jitter probability model includes: a continuous probability density function or a discrete probability distribution. S130: Based on the jitter probability model, determine the jitter value set and the jitter probability set, wherein the jitter values ​​in the jitter value set correspond to the jitter probabilities in the jitter probability set; S140: At the first and second moments of the input step signal, based on the jitter value set and the jitter probability set, determine the state transition probability matrix between the first and second moments, where the first and second moments are separated by one bit period; S150: Based on the state transition probability matrix and the step response function, an equivalent response distribution kernel is established for the target sampling time. S160: Convolve the voltage probability distribution of the previous sampling time of the target sampling time with the equivalent response distribution kernel to determine the voltage probability distribution of the target sampling time, and repeat the convolution operation at consecutive sampling times to determine the voltage probability distribution of multiple sampling times. S170: Generates a statistical eye diagram based on the voltage probability distribution at multiple sampling times.

[0019] In the above statistical eye diagram generation method, the channel can be the signal transmission path, from the transmitter to the receiver. The channel can be a transmission line or a set of circuit paths, without limitation. A step signal is input at the transmitter of the channel to obtain the channel edge template. The step signal refers to the driving form in which the level changes abruptly from a low steady state to a high steady state and remains unchanged for a sufficient time. For example, a long step with a single edge and a duration covering the effective memory length of the channel can be used. The duration of the step signal can be set according to the channel loss so that the time domain response observed at the receiver fully includes the rising and falling edge patterns, and the step response function can be determined based on the response of the step signal at the receiver. Subsequently, a jitter signal is introduced at the transmitter and a jitter probability model is established. This jitter probability model can be expressed as a continuous probability density function or a discrete probability distribution. Jitter is manifested as the uncertainty of the timing at the transmitter, such as the early or late fluctuation of the edge arrival time. When the jitter probability model is expressed as a continuous probability density function, it can be discretized into multiple jitter values ​​and multiple jitter probabilities, forming a one-to-one set of jitter values ​​and a set of jitter probabilities. When the jitter probability model itself is already discrete, its discrete jitter values ​​and their corresponding jitter probabilities can be directly taken as set elements. At the first and second time points, a state transition probability matrix for the phase jitter state at the two time points is constructed. This matrix can be used to describe the joint occurrence probability of the jitter value of the current sampling phase and the jitter value of the same phase in the previous bit period. Furthermore, at any target sampling phase, an equivalent response distribution kernel can be established based on the state transition probability matrix and the step response function. At the target sampling phase, this application shifts the step response function according to the jitter value of the current phase corresponding to the previous bit period, and performs probability weighting on different shift cases according to the state transition probability matrix to obtain the equivalent response distribution kernel corresponding to the phase, thereby reflecting the possible voltage change trend and its weight under the combined effect of edge morphology and jitter. The edge templates are shifted in time according to the jitter values ​​corresponding to the current and previous bit periods, and each shift is weighted and superimposed according to the state transition probability to determine the result that reflects the amplification / colorization effect of the channel on the transmitter jitter. Then, a recursive method is used to calculate the voltage probability distribution. The voltage probability distribution obtained from the previous sampling phase is convolved with the equivalent response distribution kernel to obtain the voltage probability distribution of the target sampling phase. This recursive process is repeated point-by-point along multiple sampling phases within a bit period until the distribution result of the entire phase is obtained. Finally, the voltage probability distributions of each sampling phase within a bit period are folded onto a unified phase axis and superimposed and visualized to generate a statistical eye diagram. In some implementations, the original probability distribution data of each phase can also be output simultaneously, providing a basis for engineering decisions such as bit error rate estimation, equalization parameters, sampling phase, and decision threshold setting.

[0020] In some embodiments of this application, the step signal is a single-edge step signal, and the step response function is a single-edge step response function.

[0021] Taking a single-edge rising-edge step signal input at the transmitting end of a channel as an example, the length of this rising-edge step signal can be determined according to the characteristics of the channel. The waveform of the response signal received at the receiving end of the channel is the single-edge step response function of the channel. For example, a 60-bit single-edge step signal can be input, with the code pattern set to a square wave signal of 011111111111… (59 ones) and a period of T. The resulting single-edge step response function is denoted as f(x).

[0022] In some embodiments of this application, when the jitter probability model is a continuous probability density function, the jitter signal is divided into multiple first jitter value subsets based on a preset jitter value segmentation; the median of the multiple first jitter values ​​in the first jitter value subset is taken as the second jitter value of the jitter value subset; and the second jitter probability of the second jitter value is determined based on the first jitter probability of the multiple first jitter values.

[0023] The jitter introduced at the transmitting end in this application is not limited to any specific type of jitter. The jitter probability model can be a continuous probability density function or a discrete probability distribution. Taking the most common and unavoidable random jitter (RJ) in the channel as an example, the continuous probability density function of this jitter can conform to a standard normal distribution, as shown in Formula 1 below: Formula 1 If the jitter probability model is an analytical continuous probability density function, it can be discretized and divided into segments based on a preset jitter value. For example, 1 million points can be generated using the normal distribution in Formula 1 above. The jitter values ​​of these 1 million points can be divided into 200 segments (i.e., 200 subsets of first jitter values). The median of multiple first jitter values ​​in each segment is taken as the jitter value of that segment (i.e., the second jitter value). The sum of the probabilities of the first jitter values ​​corresponding to all first jitter values ​​in each segment is taken as the jitter probability value corresponding to that subset of first jitter values ​​(i.e., the second jitter probability). The probability distribution of jitter is calculated in this way, and the jitter values ​​of these 200 points are recorded as the second jitter values. The corresponding second jitter probability is denoted as .in, {0, 1, ..., 200}.

[0024] In some embodiments of this application, the matrix elements of the state transition probability matrix include the joint probability of the second jitter probabilities among a plurality of second jitter values.

[0025] Without jitter at the transmitting end, the step response function at the receiving end has only one possible value, f(t), at a given time t, with a probability of 1. However, with jitter added, the step response function has multiple possible values ​​at a given time t, each with a probability corresponding to the jitter probability. Taking the aforementioned jitter probability model as an example, the step response function has 200 possible values ​​at a given time t, i.e. And the corresponding probability value is For two time points t and tT separated by one period T, a state transition probability matrix AP can be established. The number of rows and columns of the matrix is ​​the same as the number of the second jitter values. In this state transition probability matrix, each element... The value is the product of the corresponding second jitter probability, and so on. Become The probability of the jitter signal. Since the jitter signal is independent and identically distributed at each time point, the state transition probability matrix can be established as a 200×200 matrix, where the matrix elements include the joint probability of the second jitter probabilities among multiple second jitter values, that is, the matrix elements. The corresponding response value is for time t. And the response value at time t - T The probability of.

[0026] In some embodiments of this application, generating a statistical eye diagram based on the voltage probability distribution of multiple sampling times includes: folding multiple sampling times onto a phase axis with the same unit interval according to the bit period; superimposing the voltage probability distributions of the same or adjacent phases to obtain a first voltage-phase two-dimensional probability distribution, wherein the phase is the relative position of the sampling time with respect to the bit period; normalizing the first voltage-phase two-dimensional probability distribution to determine a second voltage-phase two-dimensional probability distribution; and mapping the second voltage-phase two-dimensional probability distribution to a preset color to generate a statistical eye diagram.

[0027] In some embodiments of this application, the voltage probability distributions of the same or adjacent phases are superimposed and calculated, including: summing or averaging the voltage probability distributions from different bit periods within the same phase unit; and smoothing the voltage probability distributions of adjacent phase units according to a preset weight.

[0028] In some embodiments of this application, an equivalent response distribution kernel is established for the target sampling time, including: The step response function is time-shifted based on the jitter value set to generate multiple shifted responses; The translation response is weighted and superimposed according to the matrix elements of the state transition probability matrix to obtain the equivalent response distribution kernel for the target sampling time. The equivalent response distribution kernel is used to characterize the influence of the channel on the transmitter jitter, and the weighted superposition is used to characterize the amplification effect or colorization effect of the channel on the transmitter jitter.

[0029] In the above embodiments, an equivalent response distribution kernel can be established at any target sampling time. The purpose is to convert the combined values ​​of the channel edge template and the transmitter jitter in two adjacent bit periods into a statistical response at that time. Specifically, firstly, using the single-edge step response as the edge template, for the bit period where the target sampling time is located and the corresponding sampling phase one bit period apart, the corresponding current jitter value and the jitter value of the previous period are taken from the jitter value set, respectively. The edge template is then shifted on the time axis according to these two values. Since jitter is a statistical quantity, different jitter value combinations correspond to different jitter probabilities. In this embodiment, the state transition probability matrix determined in step S140 is used as the weight. Based on the single-edge step response function, the channel response can be time-shifted using the jitter values ​​in the jitter value set to form a shifted response. Multiple shifted responses are weighted and combined according to the corresponding transition probabilities in the state transition matrix to obtain the equivalent response distribution kernel for that sampling time. This equivalent response distribution kernel reflects the influence of the channel on the transmitter jitter, and amplifies or suppresses the contribution of different translation combinations through the aforementioned weights, thus reflecting the channel's amplification (colorization) effect on jitter. For example, the formula for calculating the voltage probability distribution at the target sampling time k is shown in Formula 2: Formula 2 Taking the single-edge step signal input in this application as an example, the voltage b at a certain moment j ∈ (0,1), P b-1b0 For the code type from b -1 The probability of changing to b0. For code type b -m-1 b -m b -m+1 ...b -1 The corresponding translation response, The code pattern of the previous sampling time k-1 of the target sampling time k is b -m-1 b -m b -m+1 ...b -1 The corresponding probability value, where ⊙ represents the convolution symbol. Formula 2 allows calculation of the probability values ​​of all code patterns at all times, starting from time 0 of the single-edge step signal, thus determining the voltage probability distribution for generating the eye diagram. In this application, since the single-edge response is used, Formula 2 is simplified to Formula 3: Formula 3 That is, P b-1b0 =1 / 2, SBR is a single-bit response. The shift response S1=δ(v-SBR(t-KT)) when the code pattern is 1, and the shift response S0=δ(v) when the code pattern is 0, where SBR(t-KT)=f(t-KT)-f(tT-KT). Due to the jitter introduced at the transmitting end, the value of SBR(t-KT) changes from only one to multiple possibilities, that is, SBR(t-KT=f(t-KT-Pi)-f(tT-KT-Pj). Therefore, the voltage probability distribution at the target sampling time can be determined by formula 4: Formula 4 In some embodiments of this application, the first moment corresponds to the target sampling phase within the current bit period; the second moment corresponds to the corresponding sampling phase that is one bit period apart from the target sampling phase.

[0030] Figure 2 This diagram illustrates a schematic of a statistical eye diagram generation apparatus provided in some embodiments of this application. The generation apparatus 200 includes: a step signal input unit 210, used to input a step signal at the transmitting end of a channel and determine a step response function based on the step signal; a jitter introduction unit 220, used to introduce a jitter signal at the transmitting end and determine a jitter probability model based on the jitter signal, the jitter probability model including a continuous probability density function or a discrete probability distribution; and a determination unit 230, used to determine a jitter value set and a jitter probability set based on the jitter probability model, wherein the jitter values ​​in the jitter value set correspond to the jitter probabilities in the jitter probability set, and to determine the jitter probability at a first time and a second time in the input step signal based on the jitter value set and the jitter probability set. A rate set is used to determine the state transition probability matrix between the first time step and the second time step, wherein the first time step and the second time step are separated by one bit period; a convolution unit 240 is used to establish an equivalent response distribution kernel for the target sampling time step based on the state transition probability matrix and the step response function, and to perform a convolution operation between the voltage probability distribution of the previous sampling time step and the equivalent response distribution kernel to determine the voltage probability distribution of the target sampling time step, and to repeat the convolution operation at consecutive sampling times step to determine the voltage probability distribution of multiple sampling times step; a generation unit 250 is used to generate a statistical eye diagram based on the voltage probability distribution of multiple sampling times step.

[0031] Based on the same technical concept, this application also provides a computer-readable storage medium, including: a memory having instructions stored thereon; and a processor configured to implement the statistical eye diagram generation method provided in the above embodiments when reading the memory.

[0032] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not described in detail or in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Furthermore, the above embodiments can be freely combined as needed.

Claims

1. A method for generating statistical eye diagrams, characterized in that, include: A step signal is input at the transmitting end of the channel, and a step response function is determined based on the step signal; A jitter signal is introduced at the transmitting end, and a jitter probability model is determined based on the jitter signal. The jitter probability model includes a continuous probability density function or a discrete probability distribution. Based on the jitter probability model, a jitter value set and a jitter probability set are determined, wherein the jitter values ​​in the jitter value set correspond to the jitter probabilities in the jitter probability set; At the first and second moments of the input step signal, based on the jitter value set and the jitter probability set, a state transition probability matrix between the first and second moments is determined, wherein the first and second moments are separated by one bit period. Based on the state transition probability matrix and the step response function, an equivalent response distribution kernel is established for the target sampling time. The voltage probability distribution at the previous sampling time of the target sampling time is convolved with the equivalent response distribution kernel to determine the voltage probability distribution at the target sampling time. The convolution operation is repeated at consecutive sampling times to determine the voltage probability distribution at multiple sampling times. A statistical eye diagram is generated based on the voltage probability distribution at multiple sampling times.

2. The method for generating statistical eye diagrams according to claim 1, characterized in that, When the jitter probability model is a continuous probability density function, the jitter signal is divided into multiple first jitter value subsets based on a preset jitter value segmentation. The median of multiple first jitter values ​​in the first jitter value subset is taken as the second jitter value of the jitter value subset; A second jitter probability of the second jitter value is determined based on a first jitter probability of multiple first jitter values.

3. The method for generating statistical eye diagrams according to claim 2, characterized in that, The matrix elements of the state transition probability matrix include the joint probability of the second jitter probabilities among multiple second jitter values.

4. The method for generating statistical eye diagrams according to claim 3, characterized in that, The step signal is a single-edge step signal, and the step response function is a single-edge step response function.

5. The method for generating a statistical eye diagram according to claim 4, characterized in that, The generation of the statistical eye diagram based on the voltage probability distribution at multiple sampling times includes: The multiple sampling times are folded onto the same unit interval phase axis according to the bit period; The voltage probability distributions with the same or adjacent phases are superimposed to obtain a first voltage-phase two-dimensional probability distribution, wherein the phase is the relative position of the sampling time with respect to the bit period; The first voltage-phase two-dimensional probability distribution is normalized to determine the second voltage-phase two-dimensional probability distribution. The statistical eye diagram is generated by mapping the second voltage-phase two-dimensional probability distribution to a preset color.

6. The method for generating a statistical eye diagram according to claim 5, characterized in that, The superposition calculation of the voltage probability distributions of the same or adjacent phases includes: Sum or average the voltage probability distributions from different bit periods within the same phase unit; The voltage probability distribution of adjacent phase units is smoothed according to a preset weight.

7. The method for generating a statistical eye diagram according to any one of claims 1 to 6, characterized in that, The establishment of an equivalent response distribution kernel for the target sampling time includes: Based on the set of jitter values, the step response function is time-shifted to form multiple shifted responses; The translation response is weighted and superimposed according to the matrix elements of the state transition probability matrix to obtain the equivalent response distribution kernel for the target sampling time. The equivalent response distribution kernel is used to characterize the influence of the channel on the transmitter jitter, and the weighted superposition is used to characterize the amplification effect or colorization effect of the channel on the transmitter jitter.

8. The method for generating a statistical eye diagram according to claim 1, characterized in that, The first moment corresponds to the target sampling phase within the current bit period; The second moment corresponds to the sampling phase that is one bit period apart from the target sampling phase.

9. A device for generating statistical eye diagrams, characterized in that, include: A step signal input unit is used to input a step signal at the transmitting end of the channel and determine a step response function based on the step signal; A jitter introduction unit introduces a jitter signal at the transmitting end and determines a jitter probability model based on the jitter signal. The jitter probability model includes a continuous probability density function or a discrete probability distribution. The determining unit is configured to determine a jitter value set and a jitter probability set based on the jitter probability model, wherein the jitter values ​​in the jitter value set correspond to the jitter probabilities in the jitter probability set, and to determine a state transition probability matrix between the first time and the second time based on the jitter value set and the jitter probability set at a first time and a second time in the input step signal, wherein the first time and the second time are separated by one bit period; The convolution unit is used to establish an equivalent response distribution kernel for the target sampling time based on the state transition probability matrix and the step response function, and to perform a convolution operation between the voltage probability distribution of the previous sampling time of the target sampling time and the equivalent response distribution kernel to determine the voltage probability distribution of the target sampling time, and to repeat the convolution operation at consecutive sampling times to determine the voltage probability distribution of multiple sampling times. The generation unit is used to generate a statistical eye diagram based on the voltage probability distribution at multiple sampling times.

10. A computer-readable storage medium, characterized in that, include: A memory that stores instructions; The processor is configured to, when reading the memory, implement the method for generating a statistical eye diagram as described in any one of claims 1 to 8.

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