She is currently working on domain dynamics, 2D ferroelectrics, and nonvolatile memories based on piezoresponse force microscopy. Ni Zhong received her B.S. degree from Shanghai Institute of Ceramics, Chinese Academy of Sciences, and her Ph.D. from NARA Institute of Science and Technology (NAIST), Japan. In 2012, she joined the Key Laboratory of Polar Materials and Devices, Ministry of Education, East China Normal University as an associate professor. She is currently focusing on ferroelectric thin films/2D ferroelectrics/strongly correlated material and novel devices for next-generation computing systems.

Memristors with history‐dependent resistance are considered as artificial synapses and have potential in mimicking the massive parallelism and low‐power operation existing in the human brain. However, the state‐of‐the‐art memristors still suffer from excessive write noise, abrupt resistance variation, inherent stochasticity, poor endurance behavior, and costly energy consumption, which impedes massive neural architecture. A robust and low‐energy consumption organic three‐terminal memristor based on ferroelectric polymer gate insulator is demonstrated here. The conductance of this memristor can be precisely manipulated to vary between more than 1000 intermediate states with the highest OFF/ON ratio of ≈104. The quasicontinuous resistive switching in the MoS2 channel results from the ferroelectric domain dynamics as confirmed unambiguously by the in situ real‐time correlation between dynamic resistive switching and polarization change. Typical synaptic plasticity such as long‐term potentiation and depression (LTP/D) and spike‐timing dependent plasticity (STDP) are successfully simulated. In addition, the device is expected to experience 1 × 109 synaptic spikes with an ultralow energy consumption for each synaptic operation (less than 1 fJ, compatible with a bio‐synaptic event), which highlights its immense potential for the massive neural architecture in bioinspired networks.

Designing transparent flexible electronics with multi‐biological neuronal functions and superior flexibility is a key step to establish wearable artificial intelligence equipment. Here, a flexible ionic gel‐gated VO2 Mott transistor is developed to simulate the functions of the biological synapse. Short‐term and long‐term plasticity of the synapse are realized by the volatile electrostatic carrier accumulation and nonvolatile proton‐doping modulation, respectively. With the achievement of multi‐essential synaptic functions, an important sensory neuron, nociceptor, is perfectly simulated in our synaptic transistors with all key characteristics of threshold, relaxation, and sensitization. More importantly, this synaptic transistor exhibits high tolerance to the bending deformation, and the cycle‐to‐cycle variations of multi‐conductance states in potentiation and depression properties are maintained within 4%. This superior stability further indicates that our flexible device is suitable for neuromorphic computing. Simulation results demonstrate that high recognition accuracy of handwritten digits (>95%) can be achieved in a convolution neural network built from these synaptic transistors. The transparent and flexible Mott transistor based on electrically‐controlled VO2 metal‐insulator transition is believed to open up alternative approaches to developing highly stable synapses for future flexible neuromorphic systems.

A neuromorphic visual system integrating optoelectronic synapses to perform the in-sensor computing is triggering a revolution due to the reduction of latency and energy consumption. Here it is demonstrated that the dwell time of photon-generated carriers in the space-charge region can be effectively extended by embedding a potential well on the shoulder of Schottky energy barrier. It permits the nonlinear interaction of photocurrents stimulated by spatiotemporal optical signals, which is necessary for in-sensor reservoir computing (RC). The machine vision with the sensor reservoir constituted by designed self-powered Au/P(VDF-TrFE)/Cs 2 AgBiBr 6 /ITO devices is competent for both static and dynamic vision tasks. It shows an accuracy of 99.97% for face classification and 100% for dynamic vehicle flow recognition. The in-sensor RC system takes advantage of near-zero energy consumption in the reservoir, resulting in decades-time lower training costs than a conventional neural network. This work paves the way for ultralow-power machine vision using photonic devices.