Neural Network Grading for Hard Disk Drive Test Time Reduction
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Solution Overview
Problem
The manufacturing process of hard disc drives is hindered by lengthy and costly testing procedures, particularly due to the need for multiple tests that can take hours, such as bit-error rate and adjacent track interference tests, which often result in reworking or scrapping of drives that could be identified as failing earlier in the process, and the lack of flexibility in testing routines limits manufacturing efficiency.
Innovation Solution
The use of trained artificial neural networks to predict performance metrics like bit-error rate and adjacent track interference, allowing for earlier grading and potentially skipping unnecessary tests, thereby reducing overall test time and increasing manufacturing flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple comprehensive tests are conducted to ensure device reliability, then device grading accuracy is improved, but test time increases significantly
Solution Approach 1:
The patent applies preliminary action by using neural networks to predict test outcomes before actually performing the comprehensive tests. The system pre-evaluates devices using machine learning models that are trained on historical test data, allowing manufacturers to identify likely failures beforehand and prioritize or skip certain testing steps, thereby reducing overall test time while maintaining grading accuracy.
Solution Approach 2:
The neural network acts as an intermediary between the device testing process and the final grading decision. Instead of directly performing all comprehensive tests for every device, the system uses the neural network as a mediator to predict outcomes and make preliminary grading decisions, which then determines whether full testing is necessary, thus reducing time loss while preserving reliability.
2Manufacturing precision
If comprehensive testing procedures are performed for all devices, then manufacturing precision is maintained, but productivity decreases due to lengthy test procedures
Solution Approach 1:
The system performs preliminary evaluation using neural networks before comprehensive testing. By pre-predicting device performance metrics using trained models on historical data, the system can quickly identify devices that are likely to pass or fail, allowing manufacturers to maintain grading precision while reducing the time spent on mandatory comprehensive testing for all devices.
Solution Approach 2:
The patent changes the approach from fixed comprehensive testing to dynamic testing strategies based on predicted parameters. The neural network analyzes multiple parameters and historical data to predict device performance, allowing the testing procedure to be adjusted dynamically - reducing or eliminating certain tests for devices predicted to pass, while maintaining thorough testing for borderline cases to preserve manufacturing precision.
3Loss of substance
If devices undergo extensive testing to identify failures early, then rework and scrap rates are reduced, but the complexity of the testing routine increases
Solution Approach 1:
The neural network serves as an intermediary that simplifies the testing routine complexity by automatically analyzing multiple test parameters and historical data to predict device outcomes. This intermediary system handles the complexity of determining which devices need rework or scrapping, allowing manufacturers to focus on execution rather than decision-making complexity, thus reducing rework and scrap rates without proportionally increasing operational complexity.
Solution Approach 2:
The system implements feedback loops where test results and device performance data are continuously fed back into the neural network models for retraining and improvement. This feedback mechanism allows the system to learn from actual device performance and refine its predictions, reducing rework and scrap rates over time while the automated feedback processing manages the complexity of the testing routine.
4Ease of manufacture
If fixed testing routines are used for all devices, then process simplicity is maintained, but adaptability to different device conditions is reduced
Solution Approach 1:
The patent transforms the fixed testing routine into a dynamic, adaptive process using neural networks. The system continuously learns from actual device performance data and adjusts testing recommendations in real-time, allowing the same simple interface to handle diverse device conditions and requirements. This dynamic adaptation maintains process simplicity from the user perspective while providing high adaptability to different device scenarios.
Solution Approach 2:
The neural network-based system provides universality by handling multiple device types, conditions, and testing scenarios through a single integrated platform. The same system can adapt to different device specifications and testing requirements without requiring separate fixed routines for each scenario, thus maintaining ease of manufacture while significantly improving adaptability and versatility.
Data Source
AI summary
A method for testing and grading electronic devices includes receiving a set of testing data associated with an electronic device that is following a testing routine. Based on the set of testing data, the method includes computing a first performance metric of the electronic device by using a first artificial neural network and computing a second performance metric of the electronic device by using a second artificial neural network. Based on at least the first predicted performance metric and the second predicted performance metric, the method includes computing a grade for the electronic device.