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

arXiv: https://arxiv.org/abs/1809.10438 · HTML: https://mlsanity.com/paper/1809.10438/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Enhancing Biologically Inspired Hierarchical Temporal Memory with Hardware-Accelerated Reflex Memory](https://mlsanity.com/paper/2504.03746/) (2025)

## Abstract (arXiv)

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.