Adaptive Methods for Machine Learning-Based Testing of Integrated Circuits and Boards

Abstract

The relentless growth in information technology and artificial intelligence (AI) is placing demands on integrated circuits and boards for high performance, added functionality, and low power consumption. However, these new trends lead to high test cost and challenges associated with test planning. Machine learning (ML) provides an opportunity to overcome the challenges associated with the testing of complex systems. Taking the advantages of ML techniques, useful information can be extracted from test data logs, and this information helps facilitate the testing process for both chips and boards. In addition, the ever-growing need to achieve test-cost reduction with no test-quality degradation is driving the adoption of ML-based adaptive methods for testing. Adaptive test methods observe changes in the distribution of test data and dynamically adjust the testing process, thus reducing test cost. In this paper, we describe efficient solutions for adapting machine-learning techniques to testing and diagnosis. To reduce manufacturing cost, we select different test items for chips with different predicted quality levels. To avoid the periodic interruption of computing tasks in accelerators for AI, we describe an efficient online testing method that interrupts the regular computation only when a high defect rate is estimated. To identify board-level functional faults with high accuracy, we utilize online incremental learning and transfer learning to address the practical issues that arise when we deal with real-life test data in a high-volume production environment.