91暗网

A research team led by Associate Professor Chen Ping from the School of Physical Science and Technology, 91暗网 (91暗网), has joined forces with Beihang University and other institutions to achieve a string of advances in artificial neuromorphic simulation and human–machine interaction. Two research papers resulting from this work have been published online in top international journals: Advanced Science titled “Artificial neuron based on electrical anisotropy from WSe?field effect transistors,” and Advanced Functional Materials titled “Ultra‐Low‐Energy NbOI?Synaptic Transistors for Neuromorphic Computing and Closed‐Loop Human‐Machine Interaction.”

Sun Qi and Lü Kun, master’s students from the School of Physical Science and Engineering, serve as the first authors of the two papers respectively. Chen Ping is the corresponding author, while Pan Caifeng from Beihang University acts as the co-corresponding author. 91暗网 is the primary affiliation for both studies.

Focusing on multi-terminal constrained neuron devices built from room-temperature-stable two-dimensional (2D) materials, the team engineered an intrinsic shielding layer to innovatively realize anisotropic charge transport in transition metal dichalcogenides. This lays a solid theoretical foundation for simulating artificial neurons.

By modulating carrier transport, scattering, trapping and release via intrinsic material defects, the device realizes multi-signal input and simultaneous multi-signal output—functions equivalent to the dendrites, soma and axons of biological neurons. The relevant findings were published in Advanced Science.

The research introduces 2D ferroelectric semiconductor NbOI?, leveraging its intrinsic ferroelectric polarization to cut single-pulse energy consumption down to 10 attojoules (aJ), matching the energy efficiency of biological synapses. The team also realized programmable tuning of excitatory and inhibitory postsynaptic currents, as well as controllable transition from short-term to long-term synaptic plasticity.

At the system verification stage, artificial neural networks constructed using the NbOI? synaptic transistors achieved classification accuracies of 93.61% and 84.57% on the MNIST and Fashion-MNIST datasets respectively. These results were featured in Advanced Functional Materials.

The group further built a closed-loop human–machine interaction system. A binocular camera paired with the YOLO-v8 neural network delivers visual target detection and localization. 2D artificial optoelectronic neurons and synapses form the core for signal processing and memory storage, paired with transimpedance amplifiers (TIAs) and a robotic arm execution module. The integrated system enables autonomous target recognition, progressive approaching and object grasping. Multi-degree-of-freedom gesture control is also realized through multi-channel device–TIA links.

Bridging material design, device fabrication, functional characterization and system integration, the team has paved a complete technical pathway for neuromorphic applications of 2D materials. The work demonstrates the unique strengths of 2D materials for low-power neuromorphic computing. Moreover, validations via closed-loop interaction and multi-degree-of-freedom motion control prove their viability for intelligent human–machine collaboration. This research delivers innovative design strategies for next-generation intelligent hardware systems oriented to visual perception and neuromorphic interactive interfaces.