Machine-Learned SerDes Modeling for Cross-Environment Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for modeling serializer/deserializer (SerDes) models are complex and time-consuming, especially when simulating across various operating environments, making it difficult to ensure consistency between the actual SerDes and the modeled SerDes.
Innovation Solution
A method using machine learning technology, specifically neural networks, is employed to generate data sets including noise simulation and output measurement data, training a machine learning model, and applying it to the SerDes model to simplify the modeling process and improve accuracy across different environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SerDes modeling methods are used, then modeling accuracy can be achieved, but the process becomes complex and time-consuming
Solution Approach 1:
The patent creates a virtual SerDes model that copies the essential input-output behavior of the actual SerDes device. Instead of modeling the complete internal structure (AFE, control algorithms, etc.), the invention generates a simplified model that replicates the external characteristics through machine learning training, thereby reducing complexity while maintaining accuracy.
Solution Approach 2:
The patent replaces traditional physics-based modeling approaches with machine learning-based modeling. Instead of using complex mathematical models of analog front-end circuits and control algorithms, the invention uses neural networks and other ML models to learn the input-output relationships directly from data, substituting mechanical/circuit-level modeling with data-driven modeling.
2Measurement precision
If detailed internal structure modeling is performed, then modeling precision improves, but simulation time increases
Solution Approach 1:
The patent creates a simplified virtual model that copies only the essential input-output characteristics of the SerDes device. By training machine learning models on training data that captures the device's behavior across different operating conditions, the model achieves sufficient precision for most applications without requiring detailed internal structure, thus reducing simulation time significantly.
Solution Approach 2:
The patent applies partial action by modeling only the necessary external behavior of the SerDes device rather than its complete internal structure. The machine learning model learns from training data to provide adequate precision for the intended application scope, avoiding the excessive computational burden of full-detail modeling while maintaining sufficient accuracy.
3Ease of manufacture
If traditional modeling approaches are used, then model detail is comprehensive, but ease of manufacture decreases
Solution Approach 1:
The patent replaces complex circuit-level modeling with machine learning-based modeling. By using training data to teach ML models the SerDes behavior, the invention simplifies the modeling process and makes it more accessible to those with ML expertise but not necessarily deep analog circuit design knowledge, thereby improving ease of manufacture.
Solution Approach 2:
The patent creates a virtual model that copies the essential behavior of the SerDes device through machine learning. This approach simplifies the modeling process by focusing on input-output relationships rather than requiring detailed knowledge of internal circuit operations, making the modeling easier to implement and manufacture.
Data Source
AI summary
A method for modeling a serializer/deserializer (SerDes) model includes generating plural data sets including noise simulation data of the SerDes model and output measurement data of an actual SerDes, training a machine learning model based on the plural data sets, and applying the trained machine learning model and an estimation model to a model included in the SerDes model. The estimation model provides the noise simulation data as an input to the trained machine learning model.