Exploitation of the oxidation behaviour in an environmentally sensitive semiconductor is significant to modulate its electronic properties and develop unique applications. Here, we demonstrate a native oxidation-inspired InSe field-effect transistor as an artificial synapse in device level that benefits from the boosted charge trapping under ambient conditions. A thin InO_x layer is confirmed under the InSe channel, which can serve as an effective charge trapping layer for information storage. The dynamic characteristic measurement is further performed to reveal the corresponding uniform charge trapping and releasing process, which coincides with its surface-effect-governed carrier fluctuations. As a result, the oxide-decorated InSe device exhibits nonvolatile memory characteristics with flexible programming/erasing operations. Furthermore, an InSe-based artificial synapse is implemented to emulate the essential synaptic functions. The pattern recognition capability of the designed artificial neural network is believed to provide an excellent paradigm for ultra-sensitive van der Waals materials to develop electric-modulated neuromorphic computation architectures.

Silicon CMOS-based computing-in-memory encounters design and power challenges, especially in logic-in-memory scenarios requiring nonvolatility and reconfigurability. Here, we report a universal design for nonvolatile reconfigurable devices featuring a 2D/3D heterointegrated configuration. By leveraging the photo-controlled charge trapping/detrapping process and the partially top-gated energy band landscape, the van der Waals heterostacking achieves polarity storage and logic reconfigurable characteristics, respectively. Precise polarity tunability, logic nonvolatility, robustness against high temperature (at 85°C), and near-ideal subthreshold swing (80 mV dec ⁻¹ ) can be done. A comprehensive investigation of dynamic charge fluctuations provides a holistic understanding of the origins of nonvolatile reconfigurability (a trap level of 10 ¹³ cm ⁻² eV ⁻¹ ). Furthermore, we cascade such nonvolatile reconfigurable units into a monolithic circuit layer to demonstrate logic-in-memory computing possibilities, such as high-gain (65 at V _dd = 0.5 V) logic gates. This work provides an innovative 3D heterointegration prototype for future computing-in-memory hardware.

In over-complicated machine vision, target tracking within deep learning paradigms yields inaccurate and energy-intensive outputs. Although spiking neural networks excel at processing dynamic information, challenging tracking environments demand further enhancement in feature correlation learning for efficient target tracking. Distinct from Paired-spike-timing-dependent-plasticity-based architectures, we demonstrate a visual sensor based on van der Waals phototransistors, leveraging Triplet-spike-timing-dependent plasticity to extract bioinspired high-order correlation information, through tunable light-electric cooperation and competition effect on synaptic plasticity originating from interfacial defects-dominated persistent photoconductance phenomena. The universal Triplet-spike-timing-dependent plasticity with enhanced spatiotemporal correlation learning characteristic renders spiking neural networks with better processing capabilities for confusing object classification and dynamic tracking (90.44%) tasks, excelling particularly in seamless tracking post-occlusion, furthermore experimentally validated through hardware implementation on a 6\(\times\)6 van der Waals phototransistor array. The offers a bottom-up methodology employing device physics to guide mapping of biorational learning for high-performance dynamic tracking towards advanced machine visual technologies.

Recently, researchers have focused on optoelectronics based on two-dimensional van der Waals materials to realize multifunctional memory and neuron applications. Layered indium selenide (InSe) semiconductors satisfy various requirements as photosensitive channel materials, and enable the realization of intriguing optoelectronic applications. Herein, we demonstrate InSe photonic devices with different trends of output currents rooted in the carrier capture/release events under various gate voltages. Furthermore, we reported an increasing/flattening/decreasing synaptic weight change index (∆W_n) via a modulated gate electric field, which we use to imitate medicine-acting metaplasticity with effective/stable/ineffective features analogous to the synaptic weight change in the nervous system of the human brain. Finally, we take advantage of the low-frequency noise (LFN) measurements and the energy-band explanation to verify the rationality of carrier capture-assisted optoelectronics applied to neural simulation at the device level. Utilizing optoelectronics to simulate essential biomedical neurobehaviors, we experimentally demonstrate the feasibility and meaningfulness of combining electronic engineering with biomedical neurology.