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

TL;DR

This paper compares neuromorphic architectures like memristive LSTM, ANN, DNN, and HTM for wafer quality inspection, highlighting LSTM's superior performance and efficiency in low-power, scalable applications.

Contribution

It provides a performance analysis and comparison of various neuromorphic architectures for wafer inspection, emphasizing memristive LSTM's advantages.

Findings

LSTM outperforms other architectures in accuracy and efficiency.

Memristive devices enable low power and scalable wafer inspection systems.

LSTM has relatively low on-chip area and power consumption.

Abstract

The automated wafer inspection and quality control is a complex and time-consuming task, which can 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.