Sequential wafer map inspection via feedback loop with reinforcement learning

Wafer map defect recognition is a vital part of the semiconductor manufacturing process that requires a high level of precision. Measurement tools in such manufacturing systems can scan only a small region (patch) of the map at a time. However, this can be resource-intensive and lead to unnecessary additional costs if the full wafer map is measured. Instead, selective sparse measurements of the image save a considerable amount of resources (e.g. scanning time). Therefore, in this work, we propose a feedback loop approach for wafer map defect recognition. The algorithm aims to find sequentially the most informative regions in the image based on previously acquired ones and make a prediction of a defect type by having only these partial observations without scanning the full wafer map. To achieve our goal, we introduce a reinforcement learning-based measurement acquisition process and recurrent neural network-based classifier that takes the sequence of these measurements as an input. Additionally, we employ an ensemble technique to increase the accuracy of the prediction. As a result, we reduce the need for scanned patches by 38% having higher accuracy than the conventional convolutional neural network-based approach on a publicly available WM-811k dataset. • RL-based feedback loop approach for wafer map defect recognition is proposed. • A measuring system scans a small patch of the image and passes it to a classifier. • Confidence estimation is employed to assess the trustworthiness of the prediction. • The number of scanned patches is reduced by 38% compared to CNN-based methods. • The algorithm is validated on the publicly available WM-811k dataset.