Nonvolatile memory (NVM) devices based on two-dimensional (2D) materials have recently attracted widespread attention due to their high-density integration potential and the ability to be applied in computing-in-memory systems in the post-Moore era. Considering the high current on/off ratio, programmable threshold voltage, nonvolatile multilevel memory state, and extended logic functions, plenty of breakthroughs related to ferroelectric field-effect transistors (FeFETs), one of the most important NVM devices, have been made in the past decade. Among them, FETs coupled with organic ferroelectric films such as P(VDF-TrFE) displayed properties of remarkable robustness, easy preparation, and low cost. However, the dipoles of the P(VDF-TrFE) film cannot be flipped smoothly at low voltage, impeding the further application of organic FeFET. In this paper, we proposed a highperformance FeFET based on monolayer MoS 2 coupled with C 60 doped ferroelectric copolymer P(VDF-TrFE). The inserted C 60 molecules enhanced the alignment of the dipoles effectively at low voltage, allowing the modified device to demonstrate a large memory window (∼16 V), high current on/off ratio (>10 6 ), long retention time (>10 000 s), and remarkable endurance under the reduced operating voltage. In addition, the in situ logic application can be realized by constructing facile device interconnection without building complex complementary semiconductor circuits. Our results are expected to pave the way for future lowconsumption computing-in-memory applications based on high-quality 2D FeFETs.

Microrobots have garnered tremendous attention due to their small size, flexible movement, and potential for various in situ treatments. However, functional modification of microrobots has become crucial for their interaction with the environment, except for precise motion control. Here, a novel artificial intelligence (AI) microrobot is designed that can respond to changes in the external environment without an onboard energy supply and transmit signals wirelessly in real time. The AI microrobot can cooperate with external electromagnetic imaging equipment and enhance the local radiofrequency (RF) magnetic field to achieve a large penetration sensing depth and a high spatial resolution. The working ranges are determined by the structure of the sensor circuit, and the corresponding enhancement effect can be modulated by the conductivity and permittivity of the surrounding environment, reaching ~560 times at most. Under the control of an external magnetic field, the magnetic tail can actuate the microrobotic agent to move accurately, with great potential to realize in situ monitoring in different places in the human body, almost noninvasively, especially around potential diseases, which is of great significance for early disease discovery and accurate diagnosis. In addition, the compatible fabrication process can produce swarms of functional microrobots. The findings highlight the feasibility of the self-sensing AI microrobots for the development of in situ diagnosis or even treatment according to sensing signals.

Neuromorphic systems represent a promising avenue for the development of the next generation of artificial intelligence hardware. Machine vision, one of the cores in artificial intelligence, requires system-level support with low power consumption, low latency, and parallel computing. Neuromorphic vision sensors provide an efficient solution for machine vision by simulating the structure and function of the biological retina. Optoelectronic synapses, which use light as the main means to achieve the dual functions of photosensitivity and synapse, are the basic units of the neuromorphic vision sensor. Therefore, it is necessary to develop various optoelectronic synaptic devices to expand the application scenarios of neuromorphic vision systems. This review compares the structure and function for both biological and artificial retina systems, and introduces various optoelectronic synaptic devices based on different materials and working mechanisms. In addition, advanced applications of optoelectronic synapses as neuromorphic vision sensors are comprehensively summarized. Finally, the challenges and prospects in this field are briefly discussed.

Microrobots present great potential and wide applications in in-situ treatment and attract tremendous attention due to their small size and flexible movement. However, functional modification for microrobots became more important for their interaction with the environment, except for precise motion control. Here, we design a novel artificial intelligence (AI) microrobot, which can respond to changes in the external environment without onboard energy supplying and transmit signals wirelessly in real time. The AI microrobot can cooperate with external electromagnetic imaging equipment and enhance the local radiofrequency (RF) magnetic field to achieve a large penetration sensing depth and a high spatial resolution. The working ranges are determined by the structure of the sensor circuit and the corresponding enhancement effect can be modulated by the conductivity and permittivity of the surrounding environment, reaching ~ 560 times at most. Under the control of an external magnetic field, the magnetic tail can actuate the microrobotic agent to move accurately, with great potential to realize in-situ monitoring in different places in a human body in an almost noninvasive fashion, especially around potential diseases, which is of great significance for early disease discovery and accurate diagnosis. In addition, the compatible fabrication process provides an approach to swarms of functional microrobots. The findings highlight the feasibility of the self-sensing AI microrobot for the development of in-situ diagnosis or even treatment according to the sensing signals.