The Future of Memristive Technologies

Project ID: 2531ad1537

(You will need this ID for your application)

UCL Lead department: Electronic and Electrical Engineering (EEE)

Project Summary:

The relentless demand for computing power is driving global energy consumption to unsustainable levels. Forecasts indicate that, by 2035, the energy required to sustain current computing trends could exceed manageable limits, potentially stalling advancements in complex applications such as artificial intelligence and large-scale simulations. Addressing this challenge necessitates a fundamental rethinking of computing architectures, with a particular focus on enhancing the efficiency of memory technologies.

This PhD project aligns with a research vision focused on developing next-generation memory solutions and exploring novel computing approaches. The primary aim will be to advance Resistive RAM (ReRAM), a promising non-volatile memory technology, to a stage where it can fully replace conventional Flash memory. The successful candidate will work on overcoming both the technical and practical barriers that currently impede ReRAM’s adoption, from materials engineering to system integration, with the ultimate goal of making it a viable alternative across various applications.

This project offers a unique opportunity to engage with cutting-edge memristor technology, supported by a close partnership with Intrinsic Semiconductor Technologies Ltd., a company specialising in the commercialisation of SiOx-based memristors. The PhD candidate will benefit from access to both academic and industry expertise, covering a broad spectrum of topics including material optimisation, device design, and system and algorithmic co-design. The research will encompass a range of activities, from the fabrication, characterisation, and testing of memristive devices to simulation work, including device modelling, integrated circuit design, and algorithmic development.

Qualifications required: Candidates should have or expect to achieve an excellent degree(s) in Electronic Engineering, Physics, Computer Science or a related discipline. The ideal candidate would have experience in and passion for one or more of the following:

Materials engineering, nanotechnology, device physics
Understanding of electrical testing and materials characterisation
Machine learning