A synaptic transistor emulating the biological synaptic motion is demonstrated using the memcapacitance characteristics in a Pt/HfOx/n-indium-gallium-zinc-oxide (IGZO) memcapacitor. First, the metal-oxide-semiconductor (MOS) capacitor with Pt/HfOx/n-IGZO structure exhibits analog, polarity-dependent, and reversible memcapacitance in capacitance-voltage (C-V), capacitance-time (C-t), and voltage-pulse measurements. When a positive voltage is applied repeatedly to the Pt electrode, the accumulation capacitance increases gradually and sequentially. The depletion capacitance also increases consequently. The capacitances are restored by repeatedly applying a negative voltage, confirming the reversible memcapacitance. The analog and reversible memcapacitance emulates the potentiation and depression synaptic motions. The synaptic thin-film transistor (TFT) with this memcapacitor also shows the synaptic motion with gradually increasing drain current by repeatedly applying the positive gate and drain voltages and reversibly decreasing one by applying the negative voltages, representing synaptic weight modulation. The reversible and analog conductance change in the transistor at both the voltage sweep and pulse operations is obtained through the memcapacitance and threshold voltage shift at the same time. These results demonstrate the synaptic transistor operations with a MOS memcapacitor gate stack consisting of Pt/HfOx/n-IGZO.
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http://dx.doi.org/10.1088/1361-6528/aa6dac | 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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