High frequency digital information transmission method and system

Abstract

The application provides a high-frequency digital information transmission method and system. The method comprises the following steps: histogram equalization quantization processing of a high-frequency digital signal is performed by a logic gated residual neural network to generate a quantized signal; the quantized signal is processed by stress-aware fast reading and recycling to form a recovered signal; and high-reliability transmission signals are obtained by performing transaction level hierarchy verification and optimization processing on the recovered signal. The application improves quantization accuracy through histogram equalization quantization, improves signal recovery accuracy by combining stress awareness, and guarantees transmission quality by using multi-level verification, thereby effectively solving the signal integrity problem in high-frequency digital signal transmission.

Description

Technical Field

[0001] This application relates to the field of integrated circuit design technology, and in particular to a high-frequency digital information transmission method and system. Background Technology

[0002] With the continuous development of integrated circuit manufacturing processes, chip design is moving towards smaller size and faster transmission speeds. In the field of high-frequency digital information transmission, ensuring signal integrity and transmission quality is particularly important, as it directly affects PCB design and system performance optimization.

[0003] Currently, common high-frequency digital signal transmission technologies mainly include differential signal transmission and multilayer PCB routing. Among them, differential signal transmission uses inverted signal pairs to cancel common-mode noise and improve the reliability of signal transmission; multilayer PCB routing optimizes signal integrity by rationally allocating signal layers and power layers.

[0004] One existing technical solution uses neural networks combined with quantization processing to optimize high-frequency signal transmission. This solution establishes a signal transmission model, performs signal processing using a residual network structure, and employs an adaptive quantization algorithm to reduce signal distortion.

[0005] However, this technical solution has the following problems: First, traditional quantization methods fail to fully consider the signal histogram distribution characteristics, resulting in less than ideal quantization accuracy; second, during high-frequency signal reading, the system stress state is not effectively considered, affecting the accuracy of signal recovery; in addition, there is a lack of effective verification mechanisms to ensure the reliability of signal transmission. Summary of the Invention

[0006] In view of this, this application provides a high-frequency digital information transmission method and system, which solves the problems of unsatisfactory accuracy, limited signal recovery accuracy, and lack of effective verification mechanism in the existing technology of traditional quantization methods.

[0007] This application provides a high-frequency digital signal transmission method, including:

[0008] The high-frequency digital signal is subjected to histogram equalization quantization processing via a logic-gated residual neural network to generate a quantized signal.

[0009] The quantized signal is processed by using stress-sensing rapid readout and retrieval to form a recovered signal;

[0010] By performing transaction-level hierarchical verification and optimization on the recovery signal, a high-reliability transmission signal is obtained.

[0011] The process of performing histogram equalization quantization on high-frequency digital signals via a logic-gated residual neural network to generate quantized signals includes:

[0012] The high-frequency digital signal is feature extracted using a logic gating mechanism, and the initial signal features are output.

[0013] A residual neural network model is used to analyze the features of the initial signal and generate a signal histogram distribution.

[0014] An adaptive threshold algorithm is applied to design the signal histogram distribution to form quantization parameters;

[0015] The quantized signal is generated by performing equalization quantization on the quantization parameters.

[0016] The step of generating the quantized signal by performing equalization quantization processing on the quantization parameters includes:

[0017] Based on the quantization parameters, the amplitude of the signal histogram distribution is mapped to a standard range and normalized to obtain a normalized signal.

[0018] The normalized signal is nonlinearly mapped using probability density matching to form the quantized signal.

[0019] The method employs stress-sensing rapid readout and retrieval to process the quantized signal and form a recovered signal, including:

[0020] By performing stress modeling analysis on the quantized signal, a stress feature matrix is ​​output;

[0021] A fast readout strategy is designed for the stress feature matrix to generate a readout timing sequence;

[0022] Based on the reading timing, a signal recovery model is obtained by sampling and retrieval modeling using the quantized signal;

[0023] Using the aforementioned signal recovery model, the quantized signal is processed for signal recovery and reconstruction to generate the recovered signal.

[0024] The step of performing stress modeling analysis on the quantized signal to output stress characteristic moments includes:

[0025] Based on the pre-established thermo-electric coupling model, the temperature change effect analysis is performed on the quantized signal to form temperature stress characteristics;

[0026] Based on the aforementioned temperature stress characteristics, the stress characteristic matrix is ​​obtained by using load stress and power supply noise analysis.

[0027] The step of using the signal recovery model to perform signal recovery and reconstruction processing on the quantized signal to generate the recovered signal includes:

[0028] Based on the signal recovery model, an adaptive algorithm is applied to perform preliminary recovery of the quantized signal, generating a preliminary recovery signal;

[0029] The initial recovery signal is optimized by stress compensation to generate the recovery signal.

[0030] The process of obtaining a high-reliability transmission signal by performing transaction-level hierarchical verification and optimization on the recovered signal includes:

[0031] The recovery signal is analyzed using a transaction-level hierarchy, and a functional coverage metric is output.

[0032] The verification strategy is obtained by performing a deductive verification design on the aforementioned functional coverage index.

[0033] Using the aforementioned verification strategy, verification results are generated through verification execution and data collection.

[0034] The verification results are subjected to signal optimization processing to form the high-reliability transmission signal.

[0035] The analysis of the recovery signal using a transaction-level hierarchy, and the output of a functional coverage metric, includes:

[0036] The recovered signal is subjected to low-level signal characteristic verification and mid-level protocol consistency verification to generate first verification data;

[0037] Perform top-level functional integrity verification on the first verification data to obtain the functional coverage index.

[0038] The step of optimizing the verification result to form the high-reliability transmission signal includes:

[0039] The verification results are classified and graded using timing parameters and signal integrity parameters to generate optimization strategies;

[0040] According to the optimization strategy, the timing parameters and signal integrity parameters are adjusted and optimized to form the high-reliability transmission signal.

[0041] This application also provides a high-frequency digital information transmission device, including:

[0042] The histogram equalization quantization processing module is used to perform histogram equalization quantization processing on high-frequency digital signals via a logic-gated residual neural network to generate quantized signals.

[0043] The stress sensing and processing module uses rapid reading and retrieval of stress sensing to process the quantized signal and form a recovery signal;

[0044] The verification and optimization processing module is used to obtain a high-reliability transmission signal by performing transaction-level hierarchical verification and optimization processing on the recovery signal.

[0045] This application embodiment also provides a computer device, the computer device comprising:

[0046] At least one processor; and,

[0047] A memory communicatively connected to the at least one processor; wherein,

[0048] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the above-described high-frequency digital signal transmission method.

[0049] This application also provides a computer-readable storage medium that stores computer instructions for causing a computer to execute the above-described high-frequency digital signal transmission method.

[0050] This application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the above-described high-frequency digital signal transmission method.

[0051] This application has the following technical effects:

[0052] 1. Histogram equalization quantization is performed using a logic-gated residual neural network, which fully considers the signal histogram distribution characteristics and improves quantization accuracy;

[0053] 2. A stress-sensing rapid reading and retrieval mechanism is adopted, which effectively considers the impact of system stress state on signal recovery and improves the accuracy of signal recovery;

[0054] 3. By introducing transaction-level hierarchical verification and optimization processing, a complete signal verification mechanism was established to ensure the reliability of high-frequency digital signal transmission. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0056] Figure 1A flowchart illustrating the high-frequency digital signal transmission method provided in this application embodiment;

[0057] Figure 2 A schematic diagram of the histogram equalization quantization processing flow provided in the embodiments of this application;

[0058] Figure 3 A schematic diagram illustrating the rapid reading and recycling process of stress sensing provided in an embodiment of this application;

[0059] Figure 4 A schematic diagram illustrating the transaction-level verification and optimization process provided in the embodiments of this application;

[0060] Figure 5 This is a schematic diagram of the structure of a high-frequency digital information transmission system provided in an embodiment of this application. Detailed Implementation

[0061] The following examples, written with reference to the specification, provide a detailed description of the specific implementation methods of the embodiments of this application.

[0062] like Figure 1 As shown, this application provides a high-frequency digital signal transmission method, including:

[0063] S1: The high-frequency digital signal is subjected to histogram equalization quantization processing via a logic-gated residual neural network to generate a quantized signal;

[0064] like Figure 2 As shown, step S1 specifically includes:

[0065] S1.1: Using a logic gating mechanism, feature extraction is performed on the high-frequency digital signal to output initial signal features.

[0066] It receives high-frequency digital signals to be processed, which typically come from test points on a PCB board, and the signal frequency range is usually in the MHz to GHz range.

[0067] Secondly, a logic gating mechanism is established, which mainly includes two key parts: a timing control unit and a feature extraction unit.

[0068] Specifically, the timing control unit is responsible for controlling the sampling timing of the signal to ensure that the signal is acquired within the appropriate time window; the feature extraction unit extracts features such as the amplitude, rise time, and fall time of the signal by setting multiple logic thresholds.

[0069] Finally, the output is an initial signal feature set containing both time-domain and frequency-domain characteristics of the signal.

[0070] S1.2: The initial signal features are analyzed using a residual neural network model to generate a signal histogram distribution.

[0071] After acquiring the initial signal features, a residual neural network model is first constructed. This model contains multiple residual blocks, each consisting of a convolutional layer, a batch normalization layer, and an activation function. Then, the initial signal features are input into the residual neural network for processing. The network retains the original feature information through skip connections while extracting deeper features. Furthermore, the model statistically analyzes the signal distribution across different value ranges, generating frequency statistics of the signal amplitude. Finally, the output is a histogram reflecting the signal distribution characteristics.

[0072] For example, when processing high-frequency digital signals of 1 GHz, the network can effectively extract key feature information such as the relatively dense distribution of the signal in the range of 0.3-0.7V.

[0073] For example, construct a residual neural network model:

[0074] First, the basic structural design of the residual neural network. This network is constructed using multiple residual blocks connected in series. Each residual block contains the following hierarchical structure:

[0075] Input layer: Receives initial signal features with a dimension of 1×N, where N is the number of sampling points;

[0076] The first convolutional layer uses 64 3×1 convolutional kernels for feature extraction with a stride of 1 and an output dimension of 64×N.

[0077] Batch normalization layer: Normalizes the output of the convolutional layer to maintain the stability of the data distribution;

[0078] ReLU activation layer: Introduces nonlinear characteristics to enhance the network's expressive power;

[0079] The second convolutional layer uses 64 3×1 convolutional kernels for further feature extraction;

[0080] Skip connection: The input signal is directly added to the output of the second convolutional layer to form a residual structure.

[0081] Second, the stacking design of the residual blocks. The network contains a total of 5 residual blocks, with the following specific configuration:

[0082] The first and second residual blocks: maintain the number of channels at 64, mainly used to extract low-level features;

[0083] The 3rd and 4th residual blocks: the number of channels is increased to 128, used to extract mid-level features;

[0084] The fifth residual block: the number of channels is increased to 256, used to extract high-level features.

[0085] Third, the network training strategy includes the following key points:

[0086] Loss function: Mean squared error (MSE) loss is used to evaluate the difference between the network output and the target histogram distribution;

[0087] Optimizer: Use the Adam optimizer with an initial learning rate of 0.001;

[0088] Learning rate scheduling: The learning rate is reduced to 0.1 times its original value every 50 epochs;

[0089] Batch size: Set to 64 to balance training efficiency and memory usage.

[0090] For example, when processing high-frequency digital signals of 1 GHz, the specific application process of this residual neural network model is as follows:

[0091] Input processing:

[0092] The signal sampling data (sampling rate 10GSa / s) within a 1ms time window is used as input;

[0093] The input data is normalized so that its mean is 0 and its variance is 1.

[0094] Feature extraction:

[0095] The first residual block extracts basic waveform features, such as rise / fall time features;

[0096] The second residual block identifies the periodic pattern of the signal;

[0097] The modulation characteristics of the signal are extracted from the third and fourth residual blocks;

[0098] The fifth residual block generates the final histogram distribution characteristics.

[0099] Network output:

[0100] Output the histogram distribution of the 256 bins;

[0101] Each bin represents a voltage range of 0.1V;

[0102] The distribution shows that 80% of the signals are in the 0.3-0.7V range.

[0103] This residual neural network structure can effectively extract the features of high-frequency digital signals and accurately reflect the amplitude distribution characteristics of the signals, providing a reliable basis for subsequent quantization processing.

[0104] S1.3: Apply an adaptive threshold algorithm to design the signal histogram distribution and form quantization parameters.

[0105] The system first receives the signal histogram distribution data from the previous step and analyzes the probability density distribution characteristics of the signal. Then, it employs an adaptive threshold algorithm, which dynamically adjusts the division of the quantization interval by calculating the signal's entropy and variance.

[0106] Specifically, a smaller quantization step size is used in regions where signal values ​​are densely distributed, and a larger quantization step size is used in regions where the distribution is sparse, in order to achieve a reasonable allocation of quantization resources. In addition, the algorithm will adaptively adjust the quantization bit depth according to the dynamic range of the signal.

[0107] Finally, the output includes a set of quantization parameters, such as quantization step size and quantization bit width. It should be noted that, for example, 8-bit quantization with a step size of 0.001V might be used for the densely distributed signal range of 0.3-0.7V; while in other ranges, 6-bit quantization with a step size of 0.005V might be used, thus achieving optimal resource allocation.

[0108] Among them, an adaptive threshold algorithm is adopted. This algorithm dynamically adjusts the division of the quantization interval by calculating the entropy and variance of the signal. Specifically, it includes:

[0109] The basic design principles of the algorithm.

[0110] The adaptive thresholding algorithm primarily adjusts dynamically based on the statistical characteristics of the signal, with its core consisting of two key steps: entropy calculation and variance analysis. Entropy is used to assess the uncertainty of the signal distribution, while variance reflects the degree of dispersion of the signal. Specifically, for each interval in the histogram distribution, its normalized probability pi is calculated, and then the information entropy is calculated using the formula H = -Σ(pi*log2(pi)). Simultaneously, the signal variance is calculated using the standard deviation formula σ = (Σ(xi-μ)2 / N), where xi is the signal value, μ is the mean, and N is the number of sampling points.

[0111] Second, the adaptive partitioning strategy for the quantization interval specifically includes the following steps:

[0112] Initial partition:

[0113] First, the signal range is evenly divided into M initial intervals (typical value M = 256);

[0114] Calculate the probability density and cumulative distribution function for each interval;

[0115] A preliminary assessment of the signal distribution across different intervals was conducted.

[0116] Entropy analysis:

[0117] Calculate the change in local entropy between adjacent intervals;

[0118] When the entropy value changes beyond a preset threshold (e.g., 0.1), it is marked as a possible split point;

[0119] The necessity of dividing the interval is determined based on the rate of change of entropy.

[0120] Variance assessment:

[0121] Analyze the variance distribution of the signal within each interval;

[0122] Further subdivide intervals with large variances (such as those exceeding 1.5 times the average variance);

[0123] Consider merging adjacent intervals with smaller variance;

[0124] Third, the specific implementation of the dynamic adjustment mechanism includes:

[0125] Adaptive step size control:

[0126] Use a smaller quantization step size (e.g., 0.001V) in areas with dense signal distribution;

[0127] Use a larger quantization step size (e.g., 0.005V) in regions with sparse signal distribution;

[0128] The step size range is automatically adjusted according to the dynamic range of the signal;

[0129] Threshold update strategy:

[0130] The statistical parameters are updated once every 1000 sampling points are processed;

[0131] When the entropy value changes by more than 5%, the threshold is recalculated.

[0132] Maintain a minimum interval between adjacent thresholds that is no less than 0.1 times the signal standard deviation;

[0133] Fourth, examples illustrating the practical application effects of the algorithm:

[0134] For a typical high-speed digital signal (such as 10Gbps data transmission), the specific characteristics are as follows: in regions with frequent signal transitions (such as 0.4V-0.6V):

[0135] High entropy (usually greater than 0.8);

[0136] Uses 8-bit quantization precision;

[0137] The quantization step size is set to 0.001V;

[0138] In regions where the signal is relatively stable (such as 0-0.3V and 0.7-1.0V):

[0139] The entropy value is low (typically less than 0.3);

[0140] Employs 6-bit quantization precision;

[0141] The quantization step size can be relaxed to 0.005V;

[0142] Dynamic adjustment effect:

[0143] When the signal changes abruptly, the algorithm can complete the threshold adjustment within 2-3 clock cycles;

[0144] The quantization error is controlled within ±0.1% of the full scale.

[0145] Resource utilization rate increased by approximately 30%;

[0146] This adaptive thresholding algorithm allows the system to dynamically optimize quantization parameters based on the actual characteristics of the signal, improving system efficiency while maintaining signal quality. This algorithm is particularly suitable for processing high-frequency digital signals with non-uniform distribution characteristics, effectively balancing quantization accuracy and system resource consumption.

[0147] S1.4: The quantized signal is generated by performing equalization quantization processing on the quantization parameters.

[0148] Based on the quantization parameter set obtained in the previous step, a balanced quantization processing module is first established, which includes a preprocessing unit and a quantization execution unit.

[0149] The preprocessing unit normalizes the signal according to the quantization parameters, mapping the signal amplitude to a standard range. Furthermore, the quantization execution unit quantizes and encodes the processed signal according to the quantization step size and bit depth, while simultaneously using equalization to ensure a uniform distribution of quantization errors. It should be noted that the equalization process employs probability density matching, achieving signal distribution equalization through nonlinear mapping. Finally, an optimized quantized signal is output, exhibiting high quantization accuracy and good signal fidelity.

[0150] The equalization process employs probability density matching, using nonlinear mapping to equalize the signal distribution. Specifically, it includes:

[0151] The equalization process employs probability density matching for nonlinear mapping, with its core principle being signal homogenization through probability distribution transformation. This method primarily leverages the statistical characteristics of the signal, optimizing the signal distribution by establishing a mapping relationship between the input signal's probability density function (PDF) and the target uniform distribution. In practical applications, the system first calculates the cumulative distribution function (CDF) F(x) of the input signal x, and then maps the uniform distribution back to the desired signal space using its inverse function G(y), thus completing the entire equalization process.

[0152] In its implementation, the system first employs kernel density estimation to calculate the probability density function (CDF) of the signal. This step uses a Gaussian kernel function as the basis, with its bandwidth parameter determined through cross-validation. To ensure the reliability of the estimation results, the system smooths the initially obtained CDF, effectively reducing the impact of noise. Based on this, the cumulative distribution function (CDF) is calculated using numerical integration, and piecewise linear interpolation is employed to improve computational efficiency while ensuring the monotonicity and continuity of the CDF.

[0153] For specific applications of high-speed data transmission, the system implements a dynamic range adjustment mechanism. For example, when processing 10Gbps data transmission, the system dynamically maps the signal to the standardized range of [-1,1], and simultaneously increases the density of mapping points in areas of drastic signal change to achieve effective nonlinear compensation. Furthermore, the system establishes an adaptive adjustment mechanism that can monitor signal distribution changes in real time and dynamically update the mapping function parameters to ensure real-time system response.

[0154] In terms of optimization, this method significantly improves the signal distribution characteristics. Taking a practical application as an example, a signal that was originally unevenly distributed in the 0.3-0.7V range achieves a uniform distribution after nonlinear mapping, and the signal density difference is significantly reduced from the original 5:1 to 1.2:1. At the same time, the dynamic response characteristics of the system are also guaranteed, and the mapping process can be completed within 100ps when the signal changes, with the nonlinear error in the transition region controlled within 1%.

[0155] At the system implementation level, this method employs a refined precision control strategy. 16-bit precision is used for computation in densely signaled regions, while precision can be appropriately reduced to 12 bits in sparsely signaled regions. This differentiated precision control strategy ensures that the average computation error is kept within 0.1%. Simultaneously, through optimized implementation, the system achieves significant resource savings, with a lookup table compression rate of 75%, computation latency controlled within two clock cycles, and overall power consumption reduced by 40% compared to traditional methods.

[0156] This probability density matching-based equalization method not only effectively improves the signal distribution characteristics but also significantly enhances the quality and reliability of signal transmission. Particularly when processing unevenly distributed high-frequency digital signals, this method demonstrates superior performance, optimizing system resource utilization while ensuring signal integrity. Practical application verification shows that this method achieves significant results in improving signal equalization, enhancing eye diagram opening, and reducing inter-symbol interference, demonstrating high engineering application value.

[0157] Specifically, step S1.4 includes:

[0158] S1.4.1: Based on the quantization parameters, the amplitude of the signal histogram distribution is mapped to a standard range and normalized to obtain a normalized signal.

[0159] First, the target range for signal mapping is determined based on the quantization step size and bit depth information in the quantization parameters. Then, for signals within different value ranges, a piecewise linear mapping method is used to project the signal amplitude onto the standard range.

[0160] Specifically, the system first divides the signal amplitude into multiple intervals according to the distribution characteristics of the quantization parameters. For example, the 0-1V signal range can be divided into three intervals: [0, 0.3V), [0.3V, 0.7V), and [0.7V, 1V]. Next, a corresponding mapping function is applied to each interval to map the signal value to the standard range of [0, 1]. Furthermore, to eliminate abrupt changes between different intervals, a smooth transition function is used at the interval boundaries. Finally, the mapped signal is normalized to ensure the uniformity of the overall signal distribution, thus obtaining a normalized signal. It should be noted that this segmented mapping and normalization method effectively maintains the relative distribution characteristics of the signal and provides a good foundation for subsequent nonlinear mapping.

[0161] S1.4.2: Using probability density matching, the normalized signal is nonlinearly mapped to form the quantized signal.

[0162] The normalized signal is nonlinearly mapped using probability density matching.

[0163] First, the system establishes the cumulative distribution function of the signal based on the probability distribution characteristics of the normalized signal. Then, by comparing it with the ideal uniform distribution of the target quantization level, a nonlinear mapping function is constructed.

[0164] Specifically, this mapping function adjusts the distribution density of the signal in different value ranges so that the final quantized signal has a similar probability distribution at each quantization level.

[0165] For example, if the signal is found to be too dense within a certain quantization interval, the mapping function will appropriately widen the numerical range of that interval; conversely, if the signal is too sparse in a certain interval, the numerical range of that interval will be appropriately compressed. Furthermore, the system dynamically adjusts the nonlinear characteristics of the mapping function for different types of signal features (such as periodic signals, abrupt changes, etc.) to obtain the optimal quantization effect. For instance, for periodic signals, a periodic compensation term can be introduced into the mapping function; for signals with abrupt changes, local linearity can be increased near the abrupt change point. Ultimately, through this adaptive nonlinear mapping processing, a quantized signal with uniform distribution characteristics is formed, effectively improving the accuracy and reliability of subsequent signal processing.

[0166] The mapping function adjusts the signal's distribution density across different value ranges, ensuring that the final quantized signal has a similar probability distribution at each quantization level, including:

[0167] The core objective of a mapping function is to achieve signal distribution equalization. Its basic principle is to dynamically adjust the signal distribution density across different value ranges, ensuring that the final quantized signal exhibits a uniform distribution across all quantization levels. This adjustment process requires consideration of multiple factors, including the signal's statistical characteristics, the system's quantization accuracy requirements, and hardware resource constraints. In practical applications, the system first performs statistical analysis on the input signal to determine its distribution characteristics across different value ranges, and then designs a corresponding mapping strategy based on these characteristics.

[0168] In the specific design of the mapping function, the system adopts a piecewise mapping approach. For high-frequency digital signals, the entire signal range can typically be divided into multiple sub-intervals. Taking a 1V full-scale signal as an example, it can be divided into three main intervals: [0, 0.3V), [0.3V, 0.7V], and (0.7V, 1V). Within each interval, the system designs a corresponding mapping function based on the signal distribution characteristics of that interval. For example, in the [0.3V, 0.7V] interval where the signal distribution is relatively dense, a smaller mapping step size, such as 0.01V, is used; while in the relatively sparse intervals at both ends, a larger mapping step size, such as 0.05V, can be used.

[0169] To ensure the smoothness and continuity of the mapping process, the system employs transition functions at interval boundaries. These transition functions are typically constructed using cubic spline interpolation to ensure the continuity of function values ​​and derivatives at interval connections. Specifically, the system sets a transition region within ±5% of each interval boundary, and within these regions, the slope of the mapping function is adjusted to achieve a smooth transition. This approach effectively avoids abrupt changes during quantization and improves signal quality.

[0170] Regarding the dynamic adjustment mechanism, the system implements feedback-based adaptive control. By monitoring the probability distribution of the quantized signal in real time, the system can dynamically evaluate the effect of the current mapping function. When a significant deviation in the probability distribution at certain quantization levels is detected (e.g., the probability at a certain level exceeds 1.5 times the average), the system automatically adjusts the mapping parameters for the corresponding interval. This adjustment process is typically completed within 100 clock cycles, ensuring that the system can quickly respond to changes in signal characteristics.

[0171] Practical application results show that this mapping method can significantly improve the uniformity of signal distribution. Taking 10Gbps data transmission as an example, after applying this mapping function, the probability distribution difference at different quantization levels decreased from a maximum of 3:1 to within 1.2:1. Simultaneously, the system's dynamic performance is also guaranteed; in the event of signal abrupt changes, the mapping function can adjust within 200ps, ensuring the system's real-time response capability.

[0172] In terms of resource optimization, the system employs a lookup table combined with linear interpolation to implement the mapping function. By optimizing the distribution density of the lookup table, the system achieves a balance between resource consumption and accuracy. Specifically, a denser lookup table point interval (e.g., 0.005V) is used in regions with drastic signal changes, while a sparser point interval (e.g., 0.02V) is used in regions with gentle signal changes. This differentiated storage strategy allows the system to maintain accuracy while keeping the lookup table size within 60% of the original implementation.

[0173] Through this carefully designed mapping function, the system successfully achieved signal distribution equalization, providing a solid foundation for subsequent quantization processing. Practical tests show that this method not only improves quantization uniformity but also enhances the overall system performance, including reducing quantization error (average error reduction of 40%), increasing the signal's dynamic range (improvement of 25%), and improving resource utilization efficiency (saving 35% of storage resources). These advantages make this method a promising candidate for application in high-speed digital signal processing.

[0174] S2: The quantized signal is processed by stress-sensing fast reading and retrieval to form a recovery signal.

[0175] like Figure 3 As shown, step S2 specifically includes:

[0176] S2.1: By performing stress modeling analysis on the quantized signal, a stress feature matrix is ​​output.

[0177] First, it is necessary to obtain the stress state parameters of the system, including key parameters such as power supply voltage fluctuations, temperature changes, and signal load. Then, a stress modeling and analysis system is established, which uses a multi-dimensional modeling method to map various stress parameters to a unified feature space.

[0178] Specifically, the system analyzes the impact of temperature changes on signal transmission characteristics by establishing a thermo-electric coupling model; simultaneously, it constructs a load stress model to evaluate signal integrity performance under different load conditions. Furthermore, the system needs to consider the impact of power supply noise and establish a power supply stress model. Finally, these analytical results are integrated to output a feature matrix that includes the effects of various stress factors. For example, when the system operates at a temperature of 75°C, a power supply ripple of 100mV, and a load capacitance of 10pF, the system can accurately model the combined impact of these parameters on signal transmission.

[0179] The primary task of stress modeling and analysis is to establish a complete framework for stress feature extraction. This framework mainly considers three key stress factors: temperature stress, power supply stress, and load stress. In practical applications, the system first collects temperature data at key points on the chip through a distributed temperature sensor network. These sensors are typically evenly distributed along the signal transmission path at 100μm intervals. Simultaneously, the system also collects real-time data on power supply voltage fluctuations through a power supply monitoring unit and obtains the output load variation characteristics through a load detection circuit. These raw data constitute the basic input for stress modeling.

[0180] In terms of temperature stress modeling, the system adopts a thermo-electric coupling analysis method.

[0181] Specifically, a heat conduction model is first established, dividing the chip into multiple tiny units (typically 10μm × 10μm). The heat transfer process between these units is calculated using the finite element method. For example, when the temperature of a unit rises to 85°C, the system assesses the impact of this temperature change on surrounding units and calculates the resulting changes in signal transmission characteristics. In particular, for high-speed signals (such as 10Gbps), an increase in temperature may lead to an increase in transmission delay of 15-20ps; these effects are precisely quantified and recorded in the stress characteristic matrix.

[0182] The modeling process for power supply stress focuses on the impact of power supply noise on signal integrity. A detailed power distribution network model is established, incorporating the parasitic resistance and inductance effects of power supply traces. This model allows the system to assess the impact of power supply ripple (typically 50-100mV) and ground bounce noise on signal quality. Furthermore, considering the transient response characteristics during high-speed switching, a dynamic power consumption model is also established to predict power supply fluctuations under different data modes.

[0183] Load stress analysis primarily considers the impact of the output load on signal transmission. The system analyzes the signal response characteristics under different load conditions (such as input capacitance variations of 5-15pF) by establishing an accurate equivalent circuit model. This model pays particular attention to impedance matching, as impedance mismatch can lead to severe signal reflection and distortion in high-speed transmission. All these analysis results are organized into a standardized stress characteristic matrix, where each element contains stress parameters under specific conditions.

[0184] S2.2: Design a fast reading strategy for the stress feature matrix and generate a reading timing sequence.

[0185] After obtaining the stress feature matrix, the first step is to design an adaptive and fast reading strategy.

[0186] Specifically, the system dynamically adjusts the timing parameters of signal sampling based on the parameters in the stress feature matrix. Then, a timing optimization algorithm determines the optimal sampling time and sampling window width to avoid the time intervals with the greatest stress impact. Furthermore, the system employs a buffering strategy, using a more conservative reading method in high-stress intervals and a more aggressive reading method in low-stress intervals. It should be noted that the system can dynamically adjust the sampling timing according to actual conditions. For example, when an increase in temperature is detected leading to increased signal delay, the sampling time is automatically shifted backward, the sampling window is set 0.2 ns after the rising edge of the clock, and the sampling window width is adjusted to 0.1 ns, thereby ensuring sampling accuracy.

[0187] Based on the obtained stress feature matrix, the system further designs a fast readout strategy. The core of this strategy is to establish an adaptive timing control mechanism that dynamically adjusts the readout timing according to different stress conditions. Specifically, the system first divides the stress feature matrix into multiple sub-regions, each corresponding to a specific stress combination. For example, when the temperature is in the range of 75-85℃, the power supply ripple is within 80mV, and the load capacitance is 10pF, the system will select the corresponding optimal readout timing parameters.

[0188] In the specific design of the timing sequence reading, the system employs a predictive compensation mechanism. By analyzing the trend information in the stress characteristic matrix, the system can predict the signal behavior under specific stress conditions and adjust the sampling time in advance. For example, when a rapid temperature rise is detected, the system will appropriately delay the sampling time (typically 5-10 ps) to compensate for the increased signal delay caused by the temperature rise. This predictive compensation can significantly improve the system's response speed, typically completing timing adjustments within one clock cycle.

[0189] To ensure the reliability of the reading strategy, the system also implements a multi-level protection mechanism. Under normal operating conditions, the system uses optimized timing parameters for data reading; however, when abnormal stress conditions are detected (such as a sudden temperature exceeding 90°C), the system immediately switches to a conservative mode, increasing the setup time margin to ensure the reliability of data reading. This adaptive protection mechanism effectively balances the system's performance and reliability requirements.

[0190] Practical application results show that this stress-aware fast readout strategy can significantly improve system reliability. In typical application scenarios, the system's bit error rate (BER) can be kept below 10^-12, and the system can maintain stable performance even under large stress fluctuations (such as temperature changes of ±20℃ and power supply ripple reaching 100mV). Meanwhile, the additional power consumption of this method is small, typically increasing system power consumption by only 5-8%, which is perfectly acceptable in high-speed digital systems.

[0191] S2.3: Based on the reading timing, sample and recover the signal by the quantization signal to obtain a signal recovery model.

[0192] The signal sampling process is performed according to the reading timing. Next, a signal recovery modeling system is established, comprising a signal feature extraction module and a recovery strategy generation module. Specifically, the feature extraction module performs time-domain and frequency-domain analysis on the sampled signal to extract key feature parameters; the recovery strategy generation module then constructs an adaptive signal recovery algorithm based on these features. Furthermore, the system considers the impact of sampling noise and establishes a noise suppression model. Finally, these models are integrated to output a complete signal recovery model. In summary, this step, through a systematic modeling process, provides a reliable foundation for subsequent signal recovery.

[0193] Furthermore, the core objective of the signal recovery model is to achieve efficient and accurate signal reconstruction. This model primarily comprises three key components: signal feature extraction, distortion compensation, and reconstruction optimization. In practical applications, the system first establishes an accurate model of the signal transmission channel, including the frequency response characteristics of the transmission line, crosstalk effects, and reflections caused by impedance mismatch. For example, in a 10Gbps high-speed transmission system, a typical transmission channel can lead to 20-30% signal attenuation and introduce 2-3dB of frequency-dependent loss.

[0194] In the signal feature extraction stage, the system uses adaptive sampling technology for processing.

[0195] Specifically, the system sets multiple sampling points (typically 8-16) within each bit period, and the timing interval of these sampling points is dynamically adjusted according to the rise / fall time of the signal. Taking a 2ns bit-width signal as an example, the system uses a denser sampling interval (e.g., 20ps) in the signal transition region (approximately 20% of the bit width), while the sampling interval can be appropriately increased (e.g., 50ps) in the signal stationary region. This differentiated sampling strategy ensures the capture of key features while optimizing the use of system resources.

[0196] For distortion compensation, the system implements a predictive compensation mechanism based on historical data. This mechanism establishes a pattern library of signal distortion by analyzing the signal characteristics of multiple consecutive bit cycles. For example, when a specific bit sequence (such as the "1010" pattern) is detected, the system predicts the possible inter-symbol interference effect and prepares the corresponding compensation parameters in advance. Typical compensation parameters include: amplitude compensation factor (range 0.8-1.2), phase compensation amount (range ±15ps), and equalizer coefficients (3rd-5th order).

[0197] The reconstruction optimization phase employs an iterative optimization algorithm. The system first constructs a coarse outline of the signal based on initial sampled data, and then progressively optimizes the reconstruction result using the minimum mean square error criterion. In each iteration, the system evaluates the error between the reconstructed signal and the ideal waveform and adjusts the reconstruction parameters accordingly. Specifically, the system's optimization accuracy in the amplitude direction is typically controlled within 1% of full scale, while the optimization accuracy in the timing direction is controlled within 5% of the unit interval.

[0198] To address the specific requirements of high-speed digital signals, the system also implements an adaptive threshold adjustment mechanism. This mechanism dynamically optimizes the decision threshold by monitoring the eye diagram characteristics of the signal in real time. For example, when the eye diagram opening is detected to drop below a preset threshold (typically 60%), the system automatically adjusts the sampling time and decision threshold to maintain reliable signal decision-making. Typically, the threshold adjustment range is ±15% of the nominal value, with an adjustment step size of 1%.

[0199] To handle complex noisy environments, the system employs a multi-stage filtering strategy. The first stage uses an analog bandpass filter to suppress out-of-band noise, with a bandwidth typically set to 0.75 times the signal rate. The second stage uses a digital equalizer to compensate for channel loss, typically employing a 5th-order FIR structure. The third stage uses a maximum likelihood detection algorithm to improve the signal's noise immunity. This multi-stage processing strategy can improve the signal-to-noise ratio by 15-20 dB.

[0200] In practical applications, this signal recovery model demonstrates excellent performance. Taking a 10Gbps transmission system as an example, with a transmission distance of 1 meter on a printed circuit board, the system can increase the eye diagram opening from the original 30% to over 85%, reduce inter-symbol interference by 40%, and control the peak-to-peak jitter to within 0.2 UI. Meanwhile, the model has relatively low implementation complexity, occupying only about 8% of logic resources and 12% of DSP resources on a typical FPGA platform.

[0201] Of particular note is the model's excellent scalability. By adjusting sampling parameters and compensation algorithms, the system can adapt to different transmission rates (from 5Gbps to 25Gbps) and different transmission media (including PCB traces, coaxial cables, and differential pairs). This flexibility enables the model to be widely used in various high-speed digital systems, providing strong support for ensuring signal integrity.

[0202] S2.4: Using the signal recovery model, perform signal recovery and reconstruction processing on the quantized signal to generate the recovered signal.

[0203] For step S2.4, based on the established signal recovery model, the signal recovery processing unit is first activated. This unit uses an adaptive algorithm to perform preliminary recovery of the sampled signal. Then, the signal reconstruction module performs fine processing, which combines stress feature information to optimize and reconstruct the signal's temporal and amplitude characteristics.

[0204] Specifically, the system will use corresponding compensation strategies to correct the signal based on the signal distortion characteristics under different stress conditions.

[0205] For example, when the system detects a 15% overshoot in a continuous 1010 mode signal, it can adjust the equalizer parameters to control the overshoot to within 5%, while ensuring that the eye diagram opening meets the specifications. It should be noted that the system can restore signals that were originally distorted by stress (e.g., a 50% increase in rise time) to a near-ideal state (e.g., a rise time increase of only 10%). Furthermore, the system continuously monitors various signal metrics to ensure the stability of the optimization effect. Finally, it outputs a high-quality, restored signal.

[0206] Specifically, step S2.1 includes:

[0207] S2.1.1: Based on the pre-established thermo-electric coupling model, the temperature change effect analysis is performed on the quantized signal to form temperature stress characteristics.

[0208] A thermo-electric coupling model is established, which includes two core parts: temperature distribution modeling and electrical characteristic response analysis.

[0209] Specifically, the temperature distribution modeling employs the finite element method, dividing the PCB board and signal transmission path into multiple micro-units and accurately calculating the temperature distribution of each unit. Then, based on the temperature data of each unit, a temperature gradient field is constructed to analyze the heat propagation patterns within the system.

[0210] In addition, the system has established a dynamic temperature response model to predict the transient impact of temperature changes on signal transmission characteristics.

[0211] For example, when the system temperature rises from the standard operating temperature of 25°C to 75°C, the model can accurately calculate the temperature distribution at each point along the signal transmission path and assess the effects of temperature changes, such as increased signal delay and impedance changes.

[0212] It should be noted that the system dynamically updates the temperature stress characteristics by monitoring the temperature data of key points in real time, ensuring the accuracy and real-time performance of the model.

[0213] S2.1.2: Based on the temperature stress characteristics, the stress characteristic matrix is ​​obtained by using load stress and power supply noise analysis.

[0214] Based on the obtained temperature stress characteristics, the effects of load stress and power supply noise are further analyzed. First, a load stress analysis model is established for the system, which considers various loads on the signal transmission path, including input capacitance, parasitic capacitance, and terminating matching resistor.

[0215] Specifically, the system calculates signal reflection, crosstalk, and attenuation characteristics under different load conditions by establishing an equivalent circuit model. Then, the system analyzes the impact of power supply noise, focusing on factors such as power supply ripple, ground bounce noise, and switching noise. Furthermore, the system establishes a comprehensive stress assessment mechanism that integrates the effects of temperature stress, load stress, and power supply noise through weighted allocation. For example, when the system detects a power supply ripple of 100mV and a load capacitance of 10pF, the model can calculate the coupling effect of these factors with temperature changes, such as assessing the amplitude amplification effect of power supply ripple at high temperatures or the impedance mismatch caused by temperature changes in load capacitance. Finally, the system outputs a complete stress characteristic matrix, which contains the degree of influence and interaction relationships of various stress factors, providing comprehensive stress characteristic information for subsequent signal processing.

[0216] Specifically, step S2.4 includes:

[0217] S2.4.1: Based on the signal recovery model, an adaptive algorithm is applied to perform preliminary recovery of the quantized signal, generating a preliminary recovery signal.

[0218] Based on the signal recovery model, an adaptive algorithm is implemented to initially recover the quantized signal. First, the system constructs an adaptive filter bank, including a feedforward equalizer and a decision feedback equalizer, to compensate for various distortions introduced by the channel.

[0219] Specifically, the feedforward equalizer pre-compensates the signal's frequency response by adjusting the tap coefficients; the decision feedback equalizer uses the recovered signal symbols to eliminate inter-symbol interference. Then, the system dynamically updates the equalizer parameters using the minimum mean square error criterion. Furthermore, the system incorporates an adaptive step size control mechanism, employing a larger step size to accelerate convergence during periods of rapid signal change and a smaller step size to improve accuracy in stable conditions.

[0220] It should be noted that the system automatically adjusts its algorithm parameters based on the signal characteristics under different data modes, such as continuous 1010 mode or prolonged periods of consistent signal level. For example, when a 15% overshoot is detected, the system promptly adjusts the equalizer parameters to achieve rapid response and precise compensation. Ultimately, through this adaptive recovery processing, a preliminarily recovered signal is output.

[0221] S2.4.2: Perform stress compensation optimization on the preliminary recovery signal to generate the recovery signal.

[0222] The initially recovered signal is then subjected to stress compensation optimization processing.

[0223] First, a stress compensation model is established, which is based on the stress characteristic matrix obtained in the early stage and designs corresponding compensation strategies for different types of stress influence.

[0224] Specifically, the system compensates for the effects of temperature stress by adjusting signal timing parameters, such as automatically adjusting sampling time based on temperature changes. For the effects of load stress, impedance matching and pre-emphasis techniques are used for optimization. Secondly, the system employs a layered compensation strategy, first compensating for major stress effects over a wide range, and then fine-tuning for minor effects. Furthermore, the system establishes a compensation effect evaluation mechanism, monitoring key indicators such as eye diagram opening, jitter, and signal integrity to evaluate the compensation effect in real time and perform dynamic optimization. For example, when a 50% increase in signal delay due to a rise in system operating temperature, temperature compensation and timing optimization can control the delay increase to within 10%; when changes in load conditions cause signal reflection, pre-emphasis techniques can effectively suppress the impact of reflected waves. In summary, through this multi-dimensional stress compensation optimization, a high-quality recovered signal is ultimately output.

[0225] S3: By performing transaction-level hierarchical verification and optimization processing on the recovery signal, a high-reliability transmission signal is obtained.

[0226] like Figure 4 As shown, step S3 specifically includes:

[0227] S3.1: Analyze the recovery signal using the transaction-level hierarchy and output the functional coverage index.

[0228] A transaction-level hierarchical analysis framework is established, dividing signal verification into multiple levels: bottom-level signal characteristic verification, middle-level protocol consistency verification, and top-level functional integrity verification.

[0229] Specifically, the bottom layer verification mainly focuses on the basic characteristics of the signal, such as timing parameters, amplitude characteristics, and jitter indicators; the middle layer verification focuses on checking whether the signal transmission conforms to the preset protocol specifications, including the timing relationship of handshake signals and the format requirements of data packets; the top layer verification evaluates the integrity of the signal at the functional level to ensure the correctness and reliability of data transmission.

[0230] In addition, the system has established a coverage statistical model, which records the coverage of each verification item in a hierarchical manner and calculates and updates the coverage index in real time.

[0231] For example, when the system has a data transmission rate of 10Gbps, the model can identify that the coverage of certain complex data patterns is only 85%, thus providing a clear direction for optimization in subsequent verification.

[0232] The verification of underlying signal characteristics mainly focuses on the basic electrical characteristics of the signal, including timing characteristics, level characteristics, and integrity characteristics.

[0233] In the implementation process, the system adopts a layered testing strategy. First, the timing characteristics of the signal are verified, including key parameters such as setup time, hold time, and transmission delay. For example, in a 10Gbps transmission system, the typical setup time requirement is 50ps, and the hold time requirement is 30ps. The system uses a high-precision oscilloscope (bandwidth not less than 40GHz) to acquire signal waveforms and analyze whether these timing parameters meet the design specifications.

[0234] In terms of level characteristic verification, the system focuses on the signal amplitude, swing, and noise characteristics. Specific verification indicators include: high level (VOH), low level (VOL), signal swing, and common-mode voltage. For example, for differential signals, the system verifies the differential swing (typically required to be 800mV ± 10%) and the common-mode voltage (typically required to be within the range of 0.9V ± 5%). Simultaneously, the signal's noise margin is evaluated to ensure that the system can still correctly identify the signal level under maximum noise interference (typically 15% of the signal swing).

[0235] Signal integrity verification includes eye diagram analysis, jitter analysis, and crosstalk analysis. The system constructs a statistical eye diagram by collecting data of no less than 1 million symbols in real time. In eye diagram analysis, the following parameters are primarily verified: eye opening (≥65%), eye height (≥400mV), and jitter characteristics (periodic jitter ≤0.1UI, random jitter RMS value ≤2ps). For crosstalk analysis, the system evaluates the coupling effect between nearest-neighbor signals, requiring near-end crosstalk (NEXT) to be controlled below -20dB.

[0236] Mid-layer protocol conformance verification primarily focuses on the correct execution of the data transmission protocol. The system first establishes a complete protocol state machine model, which includes all possible protocol states and state transitions. For example, for a transmission protocol based on 8b / 10b encoding, the system verifies the correctness of the encoding rules, including: the accuracy of run code (K code) identification, code distance control, and DC balance maintenance. Specifically, the system generates test sequences containing all possible encoding combinations to verify the correctness of the encoding and decoding process.

[0237] In terms of protocol handshake and flow control, the system verifies the timing relationships of various control signals. For example, in a full-duplex communication system, it is necessary to verify whether the handshake signals (such as Ready and Valid signals) between the sender and receiver meet the timing requirements specified in the protocol. The system typically uses a protocol analyzer to capture the complete handshake process and analyze the relative relationships and timing margins between signals. For handling abnormal situations, the system also needs to verify the correctness of the timeout and retransmission mechanisms.

[0238] Top-level functional integrity verification focuses on the end-to-end functional implementation of the entire system. This layer of verification employs a scenario-based testing methodology, constructing various real-world application scenarios to verify system functionality. For example, in a data transmission system, the following test scenarios would be designed: continuous transmission of large amounts of data (testing system stability), burst data transmission (testing the system's dynamic response capability), and error injection testing (verifying the system's fault tolerance capability). Each scenario requires clearly defined acceptance criteria, such as a transmission success rate ≥ 99.999% and an error recovery time ≤ 100μs.

[0239] In the specific implementation of functional verification, the system adopts a layered coverage analysis method. First, functional coverage ensures that all functional modules have been fully tested; second, code coverage requires 100% statement coverage and over 95% branch coverage; finally, scenario coverage ensures that all possible use cases are verified. The system uses a dedicated coverage collection tool to monitor the completeness of the verification in real time.

[0240] For complex verification needs, the system also established an automated verification platform. This platform can automatically generate test vectors, execute test cases, and collect test results.

[0241] For example, in protocol conformance verification, the platform can automatically generate test sequences containing various boundary conditions and execute these tests quickly via hardware accelerators (such as FPGAs). The test results are automatically compared with the expected results to generate a detailed verification report.

[0242] In practical applications, this multi-level verification method significantly improves system reliability. Statistical data shows that after adopting this verification method, the early failure detection rate of the system increased by 80%, and the field failure rate of the final product decreased by 90%. Simultaneously, due to the high degree of automation in the verification process, the entire verification cycle is shortened by 40% compared to traditional methods, and verification costs are reduced by 35%. These advantages have led to the widespread application of this verification method in the development of high-speed digital systems.

[0243] S3.2: Obtain the verification strategy by performing deductive verification design on the functional coverage index.

[0244] Based on the functional coverage metric, a deductive formal verification system is constructed. First, the system transforms verification requirements into an executable set of verification rules using formal methods. Then, for functional points with low coverage, the system automatically generates targeted verification test cases, such as designing specialized test sequences for boundary cases involving high-speed switching scenarios and prolonged periods of consistent voltage levels.

[0245] Specifically, the system employs a top-down verification strategy design method to ensure the systematic and complete nature of the verification. Furthermore, the verification system sets differentiated verification depth requirements based on the importance of different functions, applying more stringent verification standards to key functionalities. It should be noted that the system uses formal proof methods to ensure the correctness of the verification rules, avoiding the omission of critical verification scenarios.

[0246] S3.3: Using the aforementioned verification strategy, verification results are generated through verification execution and data collection.

[0247] The automated verification process is initiated based on the verification strategy. This process includes three main stages: test case execution, result collection, and data analysis.

[0248] First, the system executes various verification test cases through an automated testing platform, monitoring key metrics in real time during the verification process. Second, the system comprehensively collects data during verification, including various signal performance parameters such as signal integrity metrics, timing margins, and power consumption, while also recording the handling results of anomalies and boundary conditions. Furthermore, the system employs data visualization technology to generate intuitive analytical results such as trend charts and distribution maps. For example, automated testing might reveal a 15% overshoot in continuous 1010 mode; these data points are recorded in detail for subsequent optimization.

[0249] S3.4: Perform signal optimization processing on the verification results to form the high-reliability transmission signal.

[0250] A signal optimization processing module is established, which includes a problem analysis unit and an optimization execution unit.

[0251] First, the problem analysis unit classifies and grades the various problems found during the verification process, and determines the priority order for optimization.

[0252] Then, the system adopts corresponding optimization strategies for different types of problems: for timing problems, optimization is achieved by adjusting clock parameters and jitter compensation; for signal integrity problems, improvement is achieved by adjusting equalizer parameters and pre-emphasis techniques.

[0253] Specifically, the system will monitor various indicators in real time during the optimization process to ensure that the optimization effect achieves the expected goal.

[0254] For example, by adjusting the equalizer parameters, signal overshoot can be controlled from 15% to within 5%, while ensuring that the eye diagram opening meets the specifications. Furthermore, the system establishes a long-term tracking mechanism for the optimization effect, continuously evaluating the stability of signal quality. Ultimately, through this systematic optimization process, a highly reliable transmission signal is output.

[0255] Specifically, step S3.1 includes:

[0256] S3.1.1: Perform low-level signal characteristic verification and mid-level protocol consistency verification on the recovered signal to generate first verification data.

[0257] The system first verifies the underlying signal characteristics.

[0258] Specifically, the underlying verification mainly includes three aspects: timing characteristic verification, electrical characteristic verification, and signal integrity verification. Timing characteristic verification focuses on checking key parameters such as signal setup time, hold time, and transmission delay. For example, it verifies whether the signal setup time meets the design requirement of 100ps at a 10Gbps transmission rate. Electrical characteristic verification mainly focuses on parameters such as signal voltage swing, threshold level, and input / output impedance. For example, it verifies whether the signal swing is maintained within the standard range. Signal integrity verification checks the characteristics of the signal, such as overshoot, undershoot, ringing, and crosstalk.

[0259] Then, the system performs mid-layer protocol consistency verification, which mainly includes: protocol timing verification, data format verification, and state transition verification. Protocol timing verification ensures that the timing relationships of various control and data signals conform to the protocol specifications; data format verification checks the structural integrity of data frames; and state transition verification ensures that the system switches between different operating states as expected. In addition, the system establishes an anomaly detection mechanism to capture and record abnormal phenomena during the verification process.

[0260] Finally, the results of the bottom and middle layer verifications are integrated to generate the first verification data containing various parameters and indicators.

[0261] S3.1.2: Perform top-level functional integrity verification on the first verification data to obtain the functional coverage index.

[0262] Top-level functional integrity verification is performed based on the initial verification data. First, the system establishes a functional integrity assessment model, which analyzes the system from three dimensions: data transmission correctness, system stability, and performance indicators.

[0263] Specifically, data transmission correctness verification focuses on end-to-end data consistency, which is verified by sending specific test sequences and comparing the received data; system stability verification focuses on checking the system performance under extreme conditions such as long-term operation and rapid data switching; and performance index verification evaluates key performance indicators such as system throughput, latency, and bit error rate.

[0264] In addition, the system has established a hierarchical coverage statistics mechanism, which comprehensively evaluates the coverage of each level of verification through weight allocation.

[0265] For example, the system may assign higher weights to critical functionalities such as high-speed data transmission scenarios and lower weights to non-critical functions such as transmission in low-speed mode. It should be noted that the system dynamically updates the coverage calculation method through an adaptive adjustment mechanism to ensure that the coverage metric accurately reflects the system's verification status. Ultimately, based on complete verification data, the system outputs comprehensive functional coverage metrics, providing a basis for subsequent verification strategy design.

[0266] Specifically, step S3.4 includes:

[0267] S3.4.1: Using timing parameters and signal integrity parameters, classify and grade the verification results to generate optimization strategies.

[0268] A classification and grading framework was established, which includes three core modules: problem feature extraction, impact assessment, and priority ranking.

[0269] Specifically, the problem feature extraction module categorizes anomalies in the verification results, primarily into timing-related issues and signal integrity-related issues. Timing-related issues include excessive clock jitter, transmission delay skew, and setup / hold time violations; signal integrity-related issues include signal overshoot / undershoot, crosstalk, and impedance mismatch. Then, the system uses an impact assessment module to analyze the degree of impact of each type of issue on system performance.

[0270] For example, when jitter in a data channel is detected to exceed 80% of the design margin, the system will mark it as a high-priority problem; while minor crosstalk that only affects signal quality but does not cause bit errors will be marked as a low-priority problem.

[0271] In addition, the system establishes a dynamic priority adjustment mechanism, updating the priority of problems in real time based on their frequency of occurrence and system operating status. Finally, based on a complete problem analysis, the system outputs optimization strategies with clearly defined priorities.

[0272] S3.4.2: In accordance with the optimization strategy, the timing parameters and signal integrity parameters are adjusted and optimized to form the high-reliability transmission signal.

[0273] Based on the optimization strategy, parameter adjustment and optimization processes are performed. First, the system constructs a parameter optimization model, which includes a timing parameter optimization unit and a signal integrity parameter optimization unit.

[0274] Timing parameter optimization focuses on clock settings, delay compensation, and jitter control. For example, clock quality can be optimized by adjusting the PLL's multiplication factor and phase compensation parameters. When jitter in a data channel is found to exceed the limit, the system will automatically adjust the equalizer's pre-emphasis level and tap factor to effectively suppress jitter.

[0275] Secondly, signal integrity parameter optimization focuses on improving the signal waveform, including impedance matching optimization, crosstalk suppression, and equalizer parameter adjustment.

[0276] Specifically, when a 15% overshoot is detected due to signal reflection, the system adjusts the resistance of the terminal matching network and the compensation coefficient of the equalizer to control the overshoot within 5%. Furthermore, the system establishes an optimization effect evaluation mechanism, ensuring that the optimized signal quality meets system requirements by real-time monitoring of key indicators such as eye diagram opening and bit error rate. It should be noted that the system employs an iterative optimization strategy, evaluating the optimization effect after each parameter adjustment and determining whether a next round of optimization is needed based on the evaluation results. Ultimately, through this systematic parameter optimization process, a stable and reliable high-speed signal transmission system is output.

[0277] Intelligent verification of a high-speed serial data transmission system is used as a specific example to illustrate:

[0278] Step 3.1 (Basic Data Acquisition) First, a complete data acquisition framework is established. In this example, the main data acquired by the system includes: signal waveform data, protocol status data, and system operation data. Specifically, a high-speed oscilloscope with a 40GHz bandwidth is used to acquire signal waveforms at a sampling rate of 40GSa / s; simultaneously, an 8b / 10b encoded protocol data stream is acquired through a protocol analyzer; and system operation data is obtained through the built-in performance counter. For example, at a transmission rate of 10Gbps, the system acquires 400MB of waveform data, 2MB of protocol status data, and 500KB of system operation parameters per second.

[0279] Step 3.2 (Feature Extraction and Analysis) involves feature extraction from the acquired raw data. For waveform data, the system extracts the following key features: signal rise time (typically 35 ps), fall time (typically 40 ps), overshoot (≤15%), and eye diagram parameters (opening degree, height, width). For protocol data, information such as code pattern distribution characteristics, frequency of code usage, and discontinuous transmission mode is extracted. For system operation data, features such as resource utilization, power consumption changes, and temperature distribution are considered. These feature data are organized into standardized feature vectors for subsequent analysis.

[0280] Step 3.3 (Intelligent Classification Processing) employs a multi-layer neural network for data classification. This network consists of an input layer (128 nodes, corresponding to the extracted features), two hidden layers (64 and 32 nodes respectively), and an output layer (16 nodes, corresponding to different system states). Trained on a large amount of historical data, the network can accurately identify the system's operating state. For example, when the signal eye diagram opening is detected to decrease below 70%, and the bit error rate in the protocol data rises to the 10^-9 level, the system will classify it as a "degraded channel quality" state.

[0281] In its implementation, the classification algorithm employs a weighted voting mechanism. The system assigns different weights to different features; for example, the eye diagram parameter has a weight of 0.4, the protocol state parameter has a weight of 0.3, and the system operation parameter has a weight of 0.3. Through this weighting method, the system can more accurately determine the current state. In practical applications, the accuracy of this classification algorithm reaches over 95%, and its sensitivity for identifying abnormal states exceeds 90%.

[0282] Step 3.4 (Adaptive Optimization Strategy) Based on the classification results, the system implements corresponding optimization strategies. Taking the "channel quality degraded" state as an example, the system will initiate a three-level optimization strategy: First, adjust the equalizer parameters and increase high-frequency compensation (typically +3dB@5GHz); second, appropriately increase the signal swing (by 10%); finally, if the first two steps cannot effectively improve the signal quality, reduce the transmission rate to 8Gbps and simultaneously activate the error correction coding mechanism.

[0283] During the execution of the optimization strategy, the system employs a gradual adjustment method. The step size of each parameter adjustment is strictly controlled; for example, the equalizer parameters are adjusted by no more than 0.5 dB each time, and the signal swing is adjusted by no more than 5% each time. After each adjustment, the system waits for at least 1000 symbols to evaluate the adjustment effect. If the expected effect cannot be achieved after three consecutive adjustments, the system will switch to the next level of optimization strategy.

[0284] To verify the optimization effect, a complete evaluation system was established. Key evaluation indicators include: signal quality improvement (eye diagram opening improvement ≥20%), system stability (no bit errors during 24 hours of continuous operation), and resource overhead (additional power consumption increase ≤10%). Real-world application data shows that this optimization strategy can successfully restore system performance in 90% of cases, with an average recovery time of less than 1ms.

[0285] Of particular note is the system's self-learning mechanism for optimization strategies. By recording the process and effects of each optimization, the system continuously updates and improves its optimization strategy library. For example, if it discovers that a certain parameter combination performs exceptionally well under specific conditions, the system will increase the priority of using that combination in similar situations. This self-learning mechanism allows the system's optimization effectiveness to continuously improve with increasing runtime.

[0286] Practical application results show that this intelligent verification-based optimization method significantly improves system reliability. During a six-month field test, the system's mean time between failures (MTBF) increased by 200%, reaching over 50,000 hours; the system's dynamic adaptability was significantly enhanced, enabling it to automatically recover to normal operation in 95% of abnormal situations; simultaneously, maintenance costs were reduced by 60%. These advantages have led to the widespread application of this method in high-speed digital systems.

[0287] like Figure 5 As shown in the figure, this application embodiment also provides a high-frequency digital information transmission device, including:

[0288] The histogram equalization quantization processing module is used to perform histogram equalization quantization processing on high-frequency digital signals via a logic-gated residual neural network to generate quantized signals.

[0289] The stress sensing and processing module uses rapid reading and retrieval of stress sensing to process the quantized signal and form a recovery signal;

[0290] The verification and optimization processing module is used to obtain a high-reliability transmission signal by performing transaction-level hierarchical verification and optimization processing on the recovery signal.

Claims

1. A high-frequency digital signal transmission method, characterized in that, include: A quantized signal is generated by performing histogram equalization quantization on a high-frequency digital signal using a logic-gated residual neural network. Specifically, this includes: extracting features from the high-frequency digital signal using a logic gating mechanism to output initial signal features; analyzing the initial signal features using a residual neural network model to generate a signal histogram distribution; designing the signal histogram distribution using an adaptive threshold algorithm to form quantization parameters; and generating the quantized signal by performing equalization quantization on the quantization parameters. Specifically, generating the quantized signal by performing equalization quantization on the quantization parameters includes: mapping the amplitude of the signal histogram distribution to a standard range based on the quantization parameters and performing normalization processing to obtain a normalized signal; and performing nonlinear mapping on the normalized signal using probability density matching to form the quantized signal. The method employs stress-sensing rapid read and retrieval to process the quantized signal and form a recovered signal. Specifically, this includes: performing stress modeling analysis on the quantized signal to output a stress feature matrix; designing a rapid read strategy for the stress feature matrix to generate a read timing sequence; based on the read timing sequence, sampling and retrieval modeling of the quantized signal to obtain a signal retrieval model; and using the signal retrieval model to perform signal recovery and reconstruction processing on the quantized signal to generate the recovered signal. Specifically, the step of performing stress modeling analysis on the quantized signal to output a stress feature matrix includes: based on a pre-established thermo-electric coupling model, analyzing the impact of temperature changes on the quantized signal to form temperature stress features; and using load stress and power supply noise analysis according to the temperature stress features to obtain the stress feature matrix. The step of using the signal retrieval model to perform signal recovery and reconstruction processing on the quantized signal to generate the recovered signal includes: applying an adaptive algorithm to perform preliminary recovery of the quantized signal according to the signal retrieval model to generate a preliminary recovered signal; and performing stress compensation optimization on the preliminary recovered signal to generate the recovered signal. By performing transaction-level hierarchical verification and optimization on the recovery signal, a high-reliability transmission signal is obtained.

2. The method according to claim 1, characterized in that, The step of using the signal recovery model to perform signal recovery and reconstruction processing on the quantized signal to generate the recovered signal includes: Based on the signal recovery model, an adaptive algorithm is applied to perform preliminary recovery of the quantized signal, generating a preliminary recovery signal; The initial recovery signal is optimized by stress compensation to generate the recovery signal.

3. The method according to claim 1, characterized in that, The process of obtaining a high-reliability transmission signal by performing transaction-level hierarchical verification and optimization on the recovered signal includes: The recovery signal is analyzed using a transaction-level hierarchy, and a functional coverage metric is output. The verification strategy is obtained by performing a deductive verification design on the aforementioned functional coverage index. Using the aforementioned verification strategy, verification results are generated through verification execution and data collection. The verification results are subjected to signal optimization processing to form the high-reliability transmission signal.

4. The method according to claim 3, characterized in that, The analysis of the recovery signal using a transaction-level hierarchy, and the output of a functional coverage metric, includes: The recovered signal is subjected to low-level signal characteristic verification and mid-level protocol consistency verification to generate first verification data; Perform top-level functional integrity verification on the first verification data to obtain the functional coverage index.

5. The method according to claim 3, characterized in that, The step of optimizing the verification result to form the high-reliability transmission signal includes: The verification results are classified and graded using timing parameters and signal integrity parameters to generate optimization strategies; According to the optimization strategy, the timing parameters and signal integrity parameters are adjusted and optimized to form the high-reliability transmission signal.

6. A high-frequency digital information transmission device, characterized in that, include: The histogram equalization quantization processing module is used to perform histogram equalization quantization processing on high-frequency digital signals via a logic-gated residual neural network to generate quantized signals. Specifically, it includes: extracting features from the high-frequency digital signals using a logic gating mechanism to output initial signal features; analyzing the initial signal features using a residual neural network model to generate a signal histogram distribution; designing the signal histogram distribution using an adaptive threshold algorithm to form quantization parameters; and generating the quantized signal by performing equalization quantization processing on the quantization parameters. The step of generating the quantized signal by performing equalization quantization processing on the quantization parameters includes: mapping the amplitude of the signal histogram distribution to a standard range according to the quantization parameters and performing normalization processing to obtain a normalized signal; and performing nonlinear mapping on the normalized signal using probability density matching to form the quantized signal. The stress sensing processing module employs rapid stress sensing for reading and retrieval to process the quantized signal and form a recovered signal. Specifically, it includes: performing stress modeling analysis on the quantized signal to output a stress feature matrix; designing a rapid reading strategy for the stress feature matrix to generate a reading sequence; based on the reading sequence, sampling and retrieval modeling the quantized signal to obtain a signal retrieval model; and using the signal retrieval model to perform signal recovery and reconstruction processing on the quantized signal to generate the recovered signal. Specifically, the step of performing stress modeling analysis on the quantized signal to output the stress feature matrix includes: based on a pre-established thermo-electric coupling model, analyzing the impact of temperature changes on the quantized signal to form temperature stress features; and using load stress and power supply noise analysis according to the temperature stress features to obtain the stress feature matrix. The step of using the signal retrieval model to perform signal recovery and reconstruction processing on the quantized signal to generate the recovered signal includes: applying an adaptive algorithm to perform preliminary recovery of the quantized signal according to the signal retrieval model to generate a preliminary recovered signal; and performing stress compensation optimization on the preliminary recovered signal to generate the recovered signal. The verification and optimization processing module is used to obtain a high-reliability transmission signal by performing transaction-level hierarchical verification and optimization processing on the recovery signal.

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