Resistive Memories Based on Semiconductor-Insulator Structures for Neuromorphic Computing

Project ID: 2531bd1681

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Research Theme: Information and Communication Technologies

Research Area(s): Microelectronic device technology
Advanced Materials
End use energy demand (energy efficient technologies)

UCL Lead department: London Centre for Nanotechnology (LCN)

Department Website

Lead Supervisor: Antonio Lombardo

Project Summary:

Brain-inspired, or neuromorphic computing, is an innovative approach to designing computer systems that mimic the structure and function of the human brain. Unlike traditional computing, which relies on binary processing and von Neumann architectures, neuromorphic systems process information like neural networks, using physical neurons and synapses. By emulating the brain’s efficient processing, this technology significantly improves efficiency in complex tasks like pattern recognition, learning, and decision-making, providing a solution to the unstainable energy demand of AI applications.

This PhD project aims to develop new resistive memories (memristors) based on semiconductor-insulator structures obtained by controlled oxidation of 2D layered materials and use them in integrated circuits.

Our recent results (https://lombardo-lab.com/publications) show that such structures are extremely promising building blocks for neuromorphic computing as they combine low energy consumption, fast switching time, environmental stability and multi-state programmability. When used to store synaptic weight in a neural networks, accuracy almost identical to GPU is achieved, however with significantly lower energy consumption.

Building on such results, this PhD project aims to design, fabricate and test resistive memories and integrated circuits based on semiconductor-oxide structures obtained via controlled oxidation of 2D layered semiconductors and to evaluate their performance for tasks such as image recognition.

The successful candidate will be part of the Nanoelectronic Devices Group (https://lombardo-lab.com/) and closely collaborate with the research group of Prof. Tony Kenyon. The candidate will work with other postgraduate students and postdoctoral research associates from the two groups.