Wafer Quality Inspection using Memristive LSTM, ANN, DNN and HTM

Abstract

The automated wafer inspection and quality control is complex and time consuming task, which can be speed up using neuromorphic memristive architectures, as a separate inspection device or integrating directly into sensors. This paper presents the performance analysis and comparison of different neuromorphic architectures for patterned wafer quality inspection and classification. The application of non-volatile memristive devices in these architectures ensures low power consumption, small on-chip area scalability. We demonstrate that Long-Short Term Memory (LSTM) outperforms other architectures for the same number of training iterations, and has relatively low on-chip area and power consumption.

Review on data-centric brain-inspired computing paradigms exploiting emerging memory devices

“…Similar research works have been carried out (Chen et al, 2015;Lee and Kim, 2019). In addition, research works on robotic controls (Abbeel et al, 2010) and drug discovery (Lavecchia, 2015) to image classification (Krizhevsky et al, 2012;Liu et al, 2016;Adam et al, 2018;Tang et al, 2020;Liu and Zeng, 2022) and language translation (Wu et al, 2016) have also been performed. These algorithms will only get more powerful, particularly given the recent explosive growth of the field of data science.…”