Using a Hybrid Machine Learning Approach for Test Cost Optimization in Scan Chain Testing

Continual technological advances have led to more complex microchip designs, which in turn, have led to the need for more complex fault testing. As a result, higher testing costs (increased test time and data volume) have emerged as well. This work examines one application of hybrid machine learning (ML) to optimize the costs of scan chain testing. We used fifty-one benchmark circuits to train the models and analyze their performances. Results produced with these circuits may not have any industrial significance, but the methods described may be useful for future work. We generated training data through MentorGraphics’s DFTAdvisor and FastScan and compiled them into files readable by the ML framework Weka. We then trained three individual ML models and evaluated their accuracies by comparing them against a test set. Finally, we created a hybrid model by combining these individual models, with different weights allotted to each model based on individual accuracy. Initial findings showed that there was a slight increase in performance by using a hybrid approach. We concluded that this method can be improved by using larger training sets and better heuristic algorithms when assigning weights. This research could be useful for the microchip industry by reducing time-to-market.