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A neuromuscular junction (NMJ) is a particularized synapse that activates muscle fibers for macro-motions, requiring more energy than computation. Emulating the NMJ is thus challenging owing to the need for both synaptic plasticity and high driving power to trigger motions. Here, we present an artificial NMJ using CuInP₂S₆(CIPS) as a gate dielectric integrated with an AlGaN/GaN-based high-electron mobility transistor (HEMT). The ferroelectricity of the CIPS is coupled with the two-dimensional electron gas channel in the HEMT, providing a wide programmable current range of 6 picoampere per millimeter to 5 milliampere per millimeter. The large output current window of the CIPS/GaN ferroelectric HEMT (FeHEMT) allows for amplifier-less actuation, emulating the biological NMJ functions of actuation and synaptic plasticity. We also demonstrate the emulation of biological oculomotor dynamics, including in situ object tracking and enhanced stimulus responses, using the fabricated artificial NMJ. We believe that the CIPS/GaN FeHEMT offers a promising pathway for bioinspired robotics and neuromorphic vision. 

Significant effort for demonstrating a gallium nitride (GaN)-based ferroelectric metal–oxide–semiconductor (MOS)-high-electron-mobility transistor (HEMT) for reconfigurable operation via simple pulse operation has been hindered by the lack of suitable materials, gate structures, and intrinsic depolarization effects. In this study, we have demonstrated artificial synapses using a GaN-based MOS-HEMT integrated with an α-In2Se3 ferroelectric semiconductor. The van der Waals heterostructure of GaN/α-In2Se3 provides a potential to achieve high-frequency operation driven by a ferroelectrically coupled two-dimensional electron gas (2DEG). Moreover, the semiconducting α-In2Se3 features a steep subthreshold slope with a high ON/OFF ratio (∼1010). The self-aligned α-In2Se3 layer with the gate electrode suppresses the in-plane polarization while promoting the out-of-plane (OOP) polarization of α-In2Se3, resulting in a steep subthreshold slope (10 mV/dec) and creating a large hysteresis (2 V). Furthermore, based on the short-term plasticity (STP) characteristics of the fabricated ferroelectric HEMT, we demonstrated reservoir computing (RC) for image classification. We believe that the ferroelectric GaN/α-In2Se3 HEMT can provide a viable pathway toward ultrafast neuromorphic computing. 

Abstract Artificial neural networks (ANNs) are widely used in numerous artificial intelligence‐based applications. However, the significant amount of data transferred between computing units and storage has limited the widespread deployment of ANN for the artificial intelligence of things (AIoT) and power‐constrained device applications. Therefore, among various ANN algorithms, quantized neural networks (QNNs) have garnered considerable attention because they require fewer computational resources with minimal energy consumption. Herein, an oxide‐based ternary charge‐trap transistor (CTT) that provides three discrete states and non‐volatile memory characteristics are introduced, which are desirable for QNN computing. By employing a differential pair of ternary CTTs, an artificial synaptic segregation with multilevel quantized values for QNNs is demostrated. The approach establishes a platform that combines the advantages of multiple states and robustness to noise for in‐memory computing to achieve reliable QNN performance in hardware, thereby facilitating the development of energy‐efficient AIoT.