Cooperative analysis method for integrity of power supply signal of high-speed transmitter

By using the method of comparative mask learning and feature modulation in high-speed links, a synergistic analysis model for power signal integrity including multiple encoders and decoders is constructed, which solves the problem of difficult to capture the nonlinear behavior of the transmitter and the impact of power supply noise in the prior art, and achieves efficient and accurate power signal integrity analysis.

CN119669766AActive Publication Date: 2025-03-21ZHEJIANG UNIV
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Patent Information

Application Number
CN202510180126.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-19
Publication Date
2025-03-21
Estimated Expiration
2045-02-19

AI Technical Summary

Technical Problem

The prior art is difficult to effectively capture the nonlinear behavior of the transmitter and the impact of power supply noise on the signal in high-speed links, resulting in insufficient analysis of signal integrity and power supply integrity.

Method used

The power signal integrity collaborative analysis method based on contrast mask learning and feature modulation is adopted to collect training data through circuit simulation, and a model including sequence encoder, non-sequence link encoder and non-autoregressive decoder is constructed, and a combined training of supervised learning and contrast learning is carried out to achieve efficient power signal integrity collaborative analysis.

Benefits of technology

This method can accurately capture the interaction between power supply noise and input signal, improve the ability to capture the nonlinear behavior of the transmitter circuit, and significantly improve the accuracy and efficiency of signal integrity and power supply integrity analysis.

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Abstract

The invention belongs to the field of high-speed communication link analysis, and discloses a high-speed transmitter power signal integrity collaborative analysis method, which comprises the following steps of: 1, acquiring training data through circuit simulation; 2, constructing a power signal integrity collaborative analysis model based on contrast mask learning and feature modulation; step 3, model training with combination of supervised learning and comparative learning; and 4, performing model reasoning based on efficient parallel decoding. According to the invention, the performance of the high-speed transmitter circuit under the influence of power supply noise can be efficiently analyzed. Through combination of comparative learning and supervised learning, robustness of feature representation is enhanced, and prediction precision of noise signals is improved.
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