Your browser doesn't support javascript.
loading
Geometrically Scalable Iontronic Memristors: Employing Bipolar Polyelectrolyte Gels for Neuromorphic Systems.
Zhang, Zhenyu; Sabbagh, Barak; Chen, Yunfei; Yossifon, Gilad.
Afiliação
  • Zhang Z; School of Mechanical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel.
  • Sabbagh B; Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, School of Mechanical Engineering, Southeast University, Nanjing 211189, China.
  • Chen Y; School of Mechanical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel.
  • Yossifon G; Faculty of Mechanical Engineering, Technion-Israel Institute of Technology, Haifa 3200003, Israel.
ACS Nano ; 18(23): 15025-15034, 2024 Jun 11.
Article em En | MEDLINE | ID: mdl-38804641
ABSTRACT
Iontronics that are capable of mimicking the functionality of biological systems within an artificial fluidic network have long been pursued for biomedical applications and ion-based intelligence systems. Here, we report on facile and robust realization of iontronic bipolar memristors featuring a three-layer polyelectrolyte gel structure. Significant memristive hysteresis of ion currents was successfully accomplished, and the memory time proved geometrically scalable from 200 to 4000 s. These characteristics were enabled by the ion concentration polarization-induced rectification ratio within the polyelectrolyte gels. The memristors exhibited memory dynamics akin to those observed in unipolar devices, while the bipolar structure notably enabled prolonged memory time and enhanced the ion conductance switching ratio with mesoscale (10-1000 µm) geometry precision. These properties endow the devices with the capability of effective neuromorphic processing with pulse-based input voltage signals. Owing to their simple fabrication process and superior memristive performance, the presented iontronic bipolar memristors are versatile and can be easily integrated into small-scale iontronic circuits, thereby facilitating advanced neuromorphic computing functionalities.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article