AI Failure Prediction for Mission Critical IC Chips
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mission-critical integrated circuit chips (MQKs) face challenges in detecting failures due to indirect effects of failure mechanisms like Bias Temperature Instability, Hot Carrier Injection, and Electromigration, leading to adverse impacts on electronic circuit performance.
Innovation Solution
A configuration that uses machine learning models to predict failure in IC chip components by analyzing data from embedded monitors, logging data in a database, and evaluating health-metrics to raise alarms and trigger remedial actions before actual failure occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used to detect MQK failures, then the device structure can be monitored, but failure detection is delayed until after adverse impacts have taken effect
Solution Approach 1:
The patent implements preliminary action by training machine learning models with historical data before deployment, and by continuously monitoring circuit parameters to predict failures before they occur. The system performs health assessments and generates maintenance alerts in advance, allowing preventive maintenance to be scheduled before actual failures impact device operation.
Solution Approach 2:
The patent replaces traditional mechanical/electrical monitoring systems with an artificial intelligence-based predictive maintenance system. Instead of using conventional threshold-based monitoring that only detects failures after they occur, the system uses machine learning models to analyze patterns in circuit parameters and predict future failures, substituting reactive monitoring with proactive prediction.
2Measurement precision
If AI/ML models are deployed for real-time failure prediction, then failure detection accuracy is improved, but computational complexity and resource requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the monitoring system into distinct components: data collection modules that gather circuit parameters, machine learning model modules that process the data, and alert generation modules that communicate predictions. This modular architecture allows each component to be optimized independently and facilitates deployment on resource-constrained embedded devices.
Solution Approach 2:
The patent introduces an intermediary layer between raw sensor data and failure predictions by implementing feature extraction and preprocessing modules. These intermediaries transform high-dimensional raw data into meaningful features that can be processed by simpler ML models, reducing computational complexity while maintaining prediction accuracy.
Data Source
AI summary
A configuration may identify an IC chip component the IC chip component comprising one of a logic block, a memory block, and a power grid. A configuration may train a machine learning model based on one or more features and one or more labels corresponding to the identified IC chip component. A configuration may generate an artificial intelligence model having characteristics comprising the trained machine learning model, the one or more features, and the one or more labels. A configuration may generate a prediction for the one or more labels based on past, present and projected one or more features. A configuration may monitor future label prediction versus a failure threshold. A configuration may generate a notification in response to the failure threshold being reached.