Dr. Saswat Kumar Ram, Assistant Professor, Dept. of ECE, SRM University-AP (Amaravati)
AI can be used to predict chip reliability before a failure occurs by learning from electrical, thermal, manufacturing, and operational data. Instead of only detecting a fault after it happens, an AI model can estimate the remaining useful life (RUL), probability of failure, and expected reliability of a chip.
A practical AI-based chip reliability framework has chip monitoring, data collection, applying AI models, predicting reliability and preventive action.
Collect chip health data:
AI can use measurements such asTemperature, Voltage, Current, Power consumption, Clock frequency, Timing delay, Leakage current, Error rate, Electromigration indicators, Transistor aging parameters, Memory errors, Interconnect degradation. These parameters can be monitored during chip operation.
Identify aging mechanisms:
AI can learn patterns associated with major chip degradation mechanisms like NBTI/PBTI for Threshold-voltage degradation, Electromigration for Current density, temperature, Transistor performance degradationTDDB for Gate-oxide degradation, Thermal aging for temperature history, IR drop for Voltage fluctuations, Interconnect aging for Delay and resistance, Memory degradation for bit/error patterns.
Predict failure before it happens:
Suppose a chip’s timing delay gradually increases than the Normal then aging happens and it may lead to failure. An ML model can learn this trend and predictProbability of failure in the next 1000 operating hours. This is much more useful than simply saying that the chip has already failed.
Suitable AI algorithms:
Different models can be used depending on the objective as Random Forest / XGBoostfor failure classification, SVM for healthy vs. degraded chip classification, ANN/DNN for nonlinear reliability prediction, LSTM/GRU for time-series aging prediction, Autoencoders for anomaly detection, CNN for spatial/thermal/power-map analysis, Transformer models long-term degradation trends, Reinforcement Learning for adaptive workload/thermal management.
Modern days chips health should be given more attention to run for a longer period without malfunction. Chip can be made more reliable by adopting AI technologies to predict its health and to avoid failure.
