Brain-Inspired Computing for Wafer Map Defect Pattern Classification

Abstract

Brain-Inspired hyperdimensional computing is a quickly emerging alternative machine-learning concept. Hypervectors with thousands of dimensions represent real-world data. Thanks to this redundancy, the system becomes robust against noise in the input data, but also resilient against faults, similar to the human brain. The light-weight operations with hypervectors are fully parallelizable enabling fast learning and inference at the edge. A classifier achieving high accuracies can be created through one-shot learning from few examples. Such a feature is particularly valuable in the area of semiconductor testing, where the number of training samples, especially for cutting-edge technology, is limited. In this work, we explore the applicability of brain-inspired hyperdimensional computing to the field of testing for the first time. With the example of wafer map defect pattern classification, we investigate the challenges and opportunities of this emerging concept.