systems, which can be integrated with terminal sensors to form intelligent sensory systems. [3-7] Information processing in biological sensory nervous systems involves billions of neurons interconnected through trillions of synapses, constituting immense neural networks. [8] Compared with traditional von Neumannbased computing architecture, neural networks greatly reduce time-and energy consumption by taking the advantage of co-location of logic and memory, hyperconnectivity, robustness, and massively parallel processing. [9] As the third-generation artificial neural network, spiking neural networks (SNNs) are inspired by biological nervous systems. They employ spiking neurons as computational units that process information with timing of spikes. [10] Therefore, SNNs provide the potential for spatiotemporal information processing with high time-and energy-efficiency. The learning and recognition functions aiming at spatiotemporal patterns have been demonstrated in SNN formed by resistive switching synaptic devices. [11] However, this synaptic device suffers from several performance limitations such as the discrete weight update, write variation, and high energy programing related to the filamentary switching mechanism. [12-17] Recently, He et al. use capacitively coupled multi-terminal neurotransistors for spatiotemporal information processing, where the neurotransistors mimic the dendritic discriminability of different spatiotemporal input sequences. [18] To physically emulate neural networks in hardware, large-scale crossbar Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are promising to process spatiotemporal information and can provide highly time-and energy-efficient computational paradigms for the Internet-of-Things and edge computing. Nonvolatile electrolyte-gated transistors (EGTs) provide prominent analog switching performance, the most critical feature of synaptic element, and have been recently demonstrated as a promising synaptic device. However, high performance, large-scale EGT arrays, and EGT application for spatiotemporal information processing in an SNN are yet to be demonstrated. Here, an oxide-based EGT employing amorphous Nb 2 O 5 and Li x SiO 2 is introduced as the channel and electrolyte gate materials, respectively, and integrated into a 32 × 32 EGT array. The engineered EGTs show a quasi-linear update, good endurance (10 6) and retention, a high switching speed of 100 ns, ultralow readout conductance (<100 nS), and ultralow areal switching energy density (20 fJ µm −2). The prominent analog switching performance is leveraged for hardware implementation of an SNN with the capability of spatiotemporal information processing, where spike sequences with different timings are able to be efficiently learned and recognized by the EGT array. Finally, this EGT-based spatiotemporal information processing is deployed to detect moving orientation in a tactile sensing system. These results provide an insight into oxide-based EGT devices for energy-efficient neuro...

No abstract