Large scale integrated IGZO crossbar memristor array based artificial neural architecture for scalable in-memory computing

• Published in: Materials today. Nano Vol. 25; p. 100441
• Main Authors: Naqi, Muhammad, Kim, Taehwan, Cho, Yongin, Pujar, Pavan, Park, Jongsun, Kim, Sunkook
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2024
• Subjects: Artificial intelligence; Artificial synapse; IGZO; memristor array; Neural networks; neuromorphic computing; Spiking neural network; IGZO; Neural networks; Spiking neural network; memristor array; Artificial intelligence; Artificial synapse; neuromorphic computing
• ISSN: 2588-8420; 2588-8420
• DOI: 10.1016/j.mtnano.2023.100441

Neuromorphic systems based on memristor arrays have not only addressed the von Neumann bottleneck issue but have also enabled the development of computing applications with high accuracy. In this study, an artificial neural architecture based on a 10 × 10 IGZO memristor array is presented to emulate synaptic dynamics for performing artificial intelligence (AI) computing with high recognition accuracy rate. The large area 10 × 10 IGZO memristor array was fabricated using the photolithography method, resulting in stable and reliable memory operations. The bipolar switching at −2 V–2.5 V, endurance of 500 cycles, retention of >104 s, and uniform Vset/Vreset operation of 100 devices were achieved by modulating the oxygen vacancy in the IGZO film. The emulation of electric synaptic dynamics was also observed, including potentiation-depression, multilevel long-term memory (LTM), and multilevel short-term memory (STM), revealing highly linear and stable synaptic functions at different modulated pulse settings. Additionally, electrical modeling (HSPICE) with vector-matrix measurements and simulation of various artificial neural network (ANN) algorithms, such as convolution neural network (CNN) and spiking neural network (SNN), were performed, demonstrating a linear increase in current accumulation with high recognition rates of 99.33 % and 86.46 %, respectively. This work provides a novel approach for overcoming the von Neumann bottleneck issue and emulating synaptic dynamics in various neural networks with high accuracy.