Woo, Kyung Seok Park, Hyungjun Ghenzi, Nestor Talin, A. Alec Jeong, Taeyoung Choi, Jung-Hae Oh, Sangheon Jang, Yoon Ho Han, Janguk Williams, R. Stanley Kumar, Suhas Hwang, Cheol Seong 2024-07-04T06:30:26Z 2024-07-04T06:30:26Z 2024-07-04 2024-07 1936-0851 https://pubs.kist.re.kr/handle/201004/150182 Neuromorphic computing promises an energy-efficient alternative to traditional digital processors in handling data-heavy tasks, primarily driven by the development of both volatile (neuronal) and nonvolatile (synaptic) resistive switches or memristors. However, despite their energy efficiency, memristor-based technologies presently lack functional tunability, thus limiting their competitiveness with arbitrarily programmable (general purpose) digital computers. This work introduces a two-terminal bilayer memristor, which can be tuned among neuronal, synaptic, and hybrid behaviors. The varying behaviors are accessed via facile control over the filament formed within the memristor, enabled by the interplay between the two active ionic species (oxygen vacancies and metal cations). This solution is unlike single-species ion migration employed in most other memristors, which makes their behavior difficult to control. By reconfiguring a single crossbar array of hybrid memristors, two different applications that usually require distinct types of devices are demonstrated - reprogrammable heterogeneous reservoir computing and arbitrary non-Euclidean graph networks. Thus, this work outlines a potential path toward functionally reconfigurable postdigital computers. English American Chemical Society Memristors with Tunable Volatility for Reconfigurable Neuromorphic Computing Article 10.1021/acsnano.4c03238 1 ACS Nano, v.18, no.26, pp.17007 - 17017 ACS Nano 18 26 17007 17017 N scie scopus 001251016300001 2-s2.0-85196764308 Chemistry, Multidisciplinary Chemistry, Physical Nanoscience & Nanotechnology Materials Science, Multidisciplinary Chemistry Science & Technology - Other Topics Materials Science Article TOTAL-ENERGY CALCULATIONS GRAPH NETWORKS DEVICES memristor reconfigurability neuromorphiccomputing reservoir computing non-Euclidean graphnetwork