The emergence of brain-inspired (neuromorphic) computing offers a promising route to overcome the limitations of conventional von Neumann architectures in artificial intelligence (AI). While traditional systems separate memory and computation—resulting in high energy consumption—the human brain integrates these functions efficiently within a compact structure. Addressing this gap, the research from Prof. HAN Suting, Associate Professor of Department of Chemistry at The Hong Kong Polytechnic University, focuses on memristor-based neuromorphic hardware, enabling AI systems that more closely emulate biological intelligence.
Memristors are two-terminal devices capable of both storing and processing information, making them ideal for in-memory computing. By continuously adjusting their conductance in response to electrical signals, they mimic the adaptive behaviour of biological synapses. This allows computation to occur directly within memory, eliminating costly data transfer and significantly improving speed and energy efficiency.
Through crossbar array architectures, memristor systems perform vector–matrix multiplication—a core neural network operation—in a highly parallel manner. This contrasts with the sequential processing of conventional systems, enabling faster and lower-power computation while supporting synapse-like functionality in hardware.
Prof. HAN’s work also incorporates biologically inspired learning mechanisms, particularly spike-timing-dependent plasticity (STDP), enabling adaptive weight updates in memristor arrays. This supports the development of spiking neural networks (SNNs), which more closely resemble natural neural systems.
At the materials level, her research explores hybrid perovskite and organic materials, where ion migration enables precise conductance modulation. By optimising crystallinity and introducing passivation layers, her team improves device performance, stability and scalability.
Beyond theory, these technologies show strong potential in real-world applications. Flexible, wearable memristor-based systems have been developed for in-sensor computing, integrating sensing, memory, and processing into a single platform. Such systems enable intelligent responses to environmental stimuli, supporting low-power, real-time AI in areas such as healthcare and robotics.
Looking ahead, her work extends to human–machine interfaces, including assistive technologies for visual impairments, reflecting a broader vision of compact, brain-like, energy-efficient systems. Together, these efforts position Prof. HAN’s research at the forefront of memristor-based neuromorphic hardware, bridging the gap between silicon systems and biological intelligence.
Source : Faculty of Science Newsletter (June 2026)
