Advancement in Soft Iontronic Resistive Memory Devices and Their Application for Neuromorphic Computing. (8th December 2022)

• Record Type: Journal Article
• Authors: Khan, Muhammad Umair; Kim, Jungmin; Chougale, Mahesh Y.; Shaukat, Rayyan Ali; Saqib, Qazi Muhammad; Patil, Swapnil R.; Mohammad, Baker; Bae, Jinho
• Is Part Of: Advanced intelligent systems. Volume 5:Number 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-08
• Subjects: aqueous electrolytes -- brain inspired -- ionic liquids -- neuromorphic computing -- resistive memory devices; Artificial intelligence -- Periodicals; Robotics -- Periodicals; Control theory -- Periodicals; 006.3
• DOI: 10.1002/aisy.202200281
• Languages: English
• ISSNs: 2640-4567

Abstract

Abstract : The aqueous electrolyte can be a deformable and stretchable liquid material for iontronic resistive memory devices. An aqueous medium makes a device closer to the brain‐like system with the movement of ions. This review paper proposes advances in liquid resistive memories and neuromorphic computing behavior to emulate electronic synapses. Primarily, the aqueous iontronic resistive memories can be used to study electrode and active layer materials and different device structures. Hence, herein, a timely and comprehensive study of these devices using ionic liquids, hydrogels, salt solutions, and soft electrodes to classify the device mechanism is presented. The filament formation is discussed in detail based on ion concentration polarization, electrode metallization, and movements of ions and charged molecules, which result in the formation of the metal dendrite. To manufacture a higher‐performance memory, device parameters should be optimized based on aqueous electrolytes, electrode materials, and other device design parameters. Aqueous electrolytes have smooth neurotransmission ability to fabricate brain‐inspired resistive memories with stable performance and device repeatability with smooth ion transmission. Aqueous electrode materials can be reliable for neural interface activities to compute electronic synapsis with electrical and chemical properties to ensure device reliability for a longer time period. Abstract : The artificial neural network is getting greater attention to developing electronic brain technology. Herein, the electrochemical memristive‐based electronic synapses which are developed using various electrolytes are summarized. According to the summary, this study paves the path toward future flexible synaptic technology.