Waveform Machine Learning Control for Optical Test Throughput
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Solution Overview
Problem
Current machine learning platforms lack flexibility to integrate into customer testing systems for testing multiple types of components, leading to increased manufacturing costs and reduced throughput due to repetitive testing iterations.
Innovation Solution
A machine learning platform that integrates into test and measurement systems, enabling closed-loop operations for optimizing processes like calibration and tuning of optical transmitters, using a neural network-based system that learns from acquired waveforms and metadata to predict optimal parameter settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional testing methods are used to test optical components on manufacturing lines, then testing coverage is comprehensive, but the number of testing iterations is high (up to 200 iterations in worst case), reducing throughput and increasing costs
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict optimal test parameters and outcomes before actual testing occurs. The system pre-processes test data, identifies likely failure modes, and prepares optimized test sequences in advance, reducing the number of iterations needed during actual manufacturing testing while maintaining comprehensive coverage
Solution Approach 2:
The patent implements feedback mechanisms where test results from previous iterations are fed back into the machine learning system to continuously improve parameter selection. The system learns from accumulated test data to refine its predictions of optimal test parameters, progressively reducing iteration counts while maintaining reliable testing coverage across multiple component types
2Productivity
If machine learning is used to speed up parameter selection and measurements, then throughput increases and costs decrease, but flexibility to test multiple types of components is limited in current platforms
Solution Approach 1:
The patent applies universality by designing a machine learning platform that can handle multiple types of optical components (transmitters, receivers, transceivers) through a unified architecture. The system uses configurable parameter sets and adaptable model structures that can be adjusted for different component types without requiring separate dedicated systems, thereby maintaining both high throughput and flexibility
Solution Approach 2:
The patent implements dynamics by making the machine learning platform configurable and adaptable through software. Test parameters, model architectures, and processing pipelines can be dynamically adjusted based on the specific component type being tested, allowing the system to optimize performance for each component while maintaining a single unified platform that serves multiple functions
Data Source
AI summary
A test and measurement system includes a machine learning system configured to communicate with a test automation system, a user interface configured to allow a user to provide one or more user inputs and to provide results to the user, and one or more processors, the one or more processors configured to execute code that causes the one or more processors to receive one or more user inputs through the user interface, the one or more user inputs at least identifying a selected machine learning system configuration to be used to configure the machine learning system, receive a waveform created by operation of a device under test, apply the configured machine learning system to analyze the waveform, and provide an output of predicted metadata about the waveform.