Multiple types of synaptic transistors that are capable of processing electrical signals similar to the biological neural system hold enormous potential for application in parallel computing, logic circuits and peripheral detection. However, most of these presented synaptic transistors are confined to a single mode of synaptic plasticity under an electrical stimulus, which tremendously limits efficient memory formation and the multifunctional integration of synaptic transistors. Here, we proposed a bi-mode electrolyte-gated synaptic transistor (BEST) with two dynamic processes, the formation of an electrical double layer (EDL) and electrochemical doping (ECD) by tuning the applied voltages, thereby allowing volatile and non-volatile behavior, which is associated with additional ion doping and nanoscale ionic transport. Benefiting from two controllable dynamic processes, we surprisingly found a third state in the transfer curves besides the "off" and "on" states. Moreover, utilizing this unique property, an artificial nociceptor with multilevel modulation of sensitivity was realized based on our bi-mode device. Finally, a haptic sensory system was constructed to exhibit robotic motion that revealed a unique threshold switching behavior, indicating the applicability to peripheral sensing circuits. Hence, the presented bi-mode synaptic transistor provides promising prospects in achieving multiple-mode integrated devices and simplifying neural circuits, which shows great potential in the development of artificial intelligence.
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http://dx.doi.org/10.1039/d1mh01061a | DOI Listing |
Microsyst Nanoeng
January 2025
State Key Laboratory of Explosion Science and Safety Protection, Beijing Institute of Technology, Ministry of Education, 100081, Beijing, China.
Recently, the biologically inspired intelligent artificial visual neural system has aroused enormous interest. However, there are still significant obstacles in pursuing large-scale parallel and efficient visual memory and recognition. In this study, we demonstrate a 28 × 28 synaptic devices array for the artificial visual neuromorphic system, within the size of 0.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
School of Materials Science and Engineering, Gyeongsang National University, Jinju, Gyeongsangnam-do 52828, Republic of Korea.
Advances in the semiconductor industry have been limited owing to the constraints imposed by silicon-based CMOS technology; hence, innovative device design approaches are necessary. This study focuses on "more than Moore" approaches, specifically in neuromorphic computing. Although MoS devices have attracted attention as neuromorphic computing candidates, their performances have been limited due to environment-induced perturbations to carrier dynamics and the formation of defect states.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
Department of Materials Science, National Engineering Lab for TFT-LCD Materials and Technologies, Fudan University, Shanghai 200433, China.
Tactile sensation and recognition in the human brain are indispensable for interaction between the human body and the surrounding environment. It is quite significant for intelligent robots to simulate human perception and decision-making functions in a more human-like way to perform complex tasks. A combination of tactile piezoelectric sensors with neuromorphic transistors provides an alternative way to achieve perception and cognition functions for intelligent robots in human-machine interaction scenarios.
View Article and Find Full Text PDFACS Nano
January 2025
School of Electrical Engineering, Korea University, Seoul 02841, Korea.
Artificial synapses for neuromorphic computing have been increasingly highlighted, owing to their capacity to emulate brain activity. In particular, solid-state electrolyte-gated electrodes have garnered significant attention because they enable the simultaneous achievement of outstanding synaptic characteristics and mass productivity by adjusting proton migration. However, the inevitable interface traps restrict the protons at the channel-electrolyte interface, resulting in the deterioration of synaptic characteristics.
View Article and Find Full Text PDFAdv Mater
January 2025
School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China.
The increasing demand for mobile artificial intelligence applications has elevated edge computing to a prominent research area. Silicon materials, renowned for their excellent electrical properties, are extensively utilized in traditional electronic devices. However, the development of silicon materials for flexible neuromorphic computing devices encounters great challenges.
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