Optical Component Qualification Using Neural TDECQ Estimation
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Solution Overview
Problem
Existing methods for assessing optical components, such as PAM-4 and QAM transmitters, are computationally intensive and time-consuming, requiring significant resources for performance qualification.
Innovation Solution
Utilizing a trained neural network to transform optical output data into performance metrics, enabling efficient and accurate assessment of optical components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standardized TDECQ methods with reference feed-forward equalizer are used to evaluate optical transmitter performance, then measurement precision is improved, but computing time and power consumption increase significantly
Solution Approach 1:
The patent pre-calculates and stores equalization results for various eye diagram conditions in a lookup table during the training phase. When evaluating a transmitter, the system directly queries the pre-computed table rather than performing iterative equalization calculations, thereby resolving the contradiction between measurement precision and computation time.
Solution Approach 2:
The patent creates a simplified copy of the complex TDECQ evaluation process by training a neural network to replicate the behavior of the reference feed-forward equalizer. The trained model serves as a lightweight surrogate that produces equivalent performance metrics without the computational burden of the original method.
2Reliability
If iterative signal processing methods are used to mitigate impairments in optical transmitters, then reliability of performance assessment is improved, but productivity of manufacturing qualification decreases
Solution Approach 1:
The patent performs comprehensive signal processing and impairment mitigation calculations in advance during the training phase, storing the results in a pre-computed lookup table. This allows rapid during-production evaluation without repeating the computationally intensive iterative processes, thereby maintaining reliability while improving manufacturing productivity.
Solution Approach 2:
The patent replaces the mechanical iterative signal processing system with a trained neural network model that has learned the complex mitigation patterns. This substitution transforms the slow, iterative computational process into a fast, direct query operation, resolving the contradiction between reliable assessment and manufacturing throughput.
3Measurement precision
If comprehensive signal processing with dynamic equalization is implemented in optical receivers, then measurement precision of optical waveforms is improved, but device complexity increases
Solution Approach 1:
The patent introduces a trained neural network model as an intermediary between the raw eye diagram data and the performance metric calculation. This intermediary has already learned the complex signal processing relationships during training, allowing the actual receiver to use a simpler, direct query process while maintaining high measurement precision.
Solution Approach 2:
The patent moves the complex signal processing and equalization operations to the training phase, where they can be performed comprehensively without affecting the actual receiver design. The pre-computed knowledge is then embedded in the neural network model, allowing simple deployment in production receivers while maintaining high measurement precision.
Data Source
AI summary
A method comprising: receiving optical output data of an optical device; supplying the optical output data to a trained neural network configured to transform optical output data to optical performance metrics; and executing the trained neural network to transform the supplied optical output data to optical performance metrics for the optical device.