Efficient Defect Identification via Oxide Memristive Crossbar Array Based Morphological Image Processing

Defect identification has been a significant task in various fields to prevent the potential problems caused by imperfection. There is great attention for developing technology to accurately extract defect information from the image using a computing system without human error. However, image analysis using conventional computing technology based on Von Neumann structure is facing bottlenecks to efficiently process the huge volume of input data at low power and high speed. Herein efficient defect identification is demonstrated via a morphological image process with minimal power consumption using an oxide transistor and a memristor-based crossbar array that can be applied to neuromorphic computing. Using a hardware and software codesigned neuromorphic system combined with a dynamic Gaussian blur kernel operation, an enhanced defect detection performance is successfully demonstrated with about 10(4) times more power-efficient computation compared to the conventional complementary metal-oxide semiconductor (CMOS)-based digital implementation. It is believed the back end of line (BEOL)-compatible all-oxide-based memristive crossbar array provides the unique potential toward universal artificial intelligence of things (AIoT) applications where conventional hardware can hardly be used.

Automated summary

Efficient defect identification is demonstrated through morphological image processing with minimal power consumption, utilizing an oxide transistor and memristor-based crossbar array for neuromorphic computing. A co-designed neuromorphic system with dynamic Gaussian blur kernel operation shows enhanced defect detection performance, achieving about 10^4 times more power-efficient computation compared to conventional CMOS-based digital implementation. The all-oxide-based memristive crossbar array offers unique potential for universal AIoT applications where conventional hardware is not suitable.