Based on metal oxides, a material system rich in complex ionic dynamics, thermal and electrical effects and coupling effects, our group has developed a variety of artificial neuromorphic devices, which can efficiently complete various bionic tasks and brain-like computing. Through careful design of the proportions of various components in the resistive materials and the geometry of the device, we can realize a variety of complex biological nervous system behaviors in small-scale nanodevices. The devices that have been designed, manufactured and functionally verified include: NbOx neurons with behaviors such as leakage, accumulation and firing; ZnO-EMIM artificial dendritic devices with long- and short-term plasticity at the same time; YSZ-based astrocyte devices; ultra-low-power artificial devices with power consumption of only 30fJ/spike synaptic device. Based on these novel neuromorphic devices, our group has further realized complex biological neural functions including time series analysis, associative memory and so on. Research results related to this direction have been published many times in top journals in the field, such as Nature Communications, Advanced Materials, etc.

In view of the diversity of computing methods in artificial intelligence algorithms, our group combines the internal dynamic characteristics of memristors with the advantages of memristor arrays in in-memory computing, and efficiently implements a variety of expensive operations in traditional computing platforms, including random number generation, matrix-vector multiplication, matrix attenuation and so on. As a result, we have achieved extremely low power consumption in many different types of artificial intelligence hardware computing systems. The current representative systems mainly include: the reservoir computing system based on the short-term plasticity of the two-dimensional ferroelectric material α-In2Se3, which can efficiently process complex timing information with extremely low power consumption; the phase-change memory(PCM)-based eligibility trace calculation system, which can efficiently implement the eligibility trace mechanism by using the attenuation caused by PCM conductance drift and can effectively accelerate the training process of reinforcement learning; the optimization problem solving system based on long-term plasticity TaOx memristor, etc. The research results in this direction have been published many times in top journals and conferences in the field of microelectronics such as Science Advances, Advanced Materials, IEDM, etc.

For high-efficiency neural network processing hardware, our group has studied high-performance resistive memory integration technology for in-memory computing, multi-value storage devices, efficient read-write and computing circuits, and system-level chip architecture, focusing on device performance optimization and Key issues in in-memory computing architecture, such as advanced process integration, analog-digital interface circuit design, and software-hardware co-design.