Voltage-Controlled Reconfigurable Boolean Logic Enabled by a β-Ga 2 O 3 Artificial Optoelectronic Synapse
Source abstract
Abstract
Reconfigurable Boolean logics have garnered significant attention due to their potential to meet growing requirements of low energy consumption, short time delay, and high integrated circuit density in digital systems. Synaptic devices present a promising candidate toward reconfigurable Boolean logic gates by adjusting terminal biases, particularly working with optical stimulus. In this work, an optoelectronic synaptic device based on a β-Ga 2 O 3 metal-oxide–semiconductor field effect transistor (MOSFET) for reconfigurable Boolean logic gates is demonstrated via a trap-carrier modulation scheme. Synaptic functionalities including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and transition from short-term memory (STM) to long-term memory (LTM) are investigated. For a single EPSC, the β-Ga 2 O 3 optoelectronic synaptic device exhibits low energy consumption ( E con ) and excellent noise immunity. In addition, AND, OR, and NOT gates are implemented by tuning terminal voltages. Combing the reconfigurable logic functions and synaptic behaviors of the β-Ga 2 O 3 MOSFET, two typical applications, namely, handwriting number recognition and image encryption, are explicitly investigated. The validation of trap-modulated β-Ga 2 O 3 synaptic devices for reconfigurable Boolean logic gates paves a solid path for high-performance digital systems.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ga2O3 and related materials · Semiconductor materials and devices · Photocathodes and Microchannel Plates
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Science and Technology
ShanghaiTech University
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences
School of Microelectronics
University of Chinese Academy of Sciences
College of Materials Science and Engineering
Donghua University
Shanghai Engineering Research Center of Energy Efficient and Custom AI IC
Funding
Science and Technology Commission of Shanghai Municipality · National Natural Science Foundation of China
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