Optoelectronic AI processors for cryogenic and space electronics

About the Project

Supervisory team: Dr Firman Simanjuntak

This PhD project aims to develop error-tolerant AI processors for cryogenic and space electronics using optomemristor technology, which mimics neural networks and offers ultra-fast, low-power computing. Collaboration with Von Ardenne GmbH provides industrial insights into material processing and photonic integration for advanced chip design.

Cryogenic processors are gaining interest due to the rising demand for cryogenic and space electronics. However, today’s processors are based on CMOS technology that is prone to freeze attacks and magnetic interference at low temperatures. In this project, you will invent a novel building block of processors based on memristor technology to achieve error-tolerant AI chips. Memristor is an emerging memory technology that can mimic a biological neural network, rendering low-powered neuromorphic computing. Memristor can also be programmed with optical stimulation, thus called optomemristor, demonstrating its unique feasibility to be integrated with photonics for ultra-fast operation.

Von Ardenne GmbH, Germany (VA) will be the industrial partner of this project, and you will have the opportunity to visit VA facilities and collaborate with VA engineers who can provide support on materials processing and characterisations.

In the 1st year:

• you will build your first optomemristor chip prototype utilising Southampton’s and Von Ardenne’s state-of-the-art cleanroom facility
• you will learn nanofabrication (thin film engineering and lithography technique) to fabricate a wafer-scale massive array of optomemristor and surface/interface analysis (advanced microscopy and spectroscopy tools) to evaluate the quality of your prototype.

In the 2nd year:

• you will develop a protocol to encode data via laser pulses under a magnetic field at cryogenic temperatures
• you will learn electrical characterisation techniques to evaluate the synaptic capability and performance of your prototype.

In the 3rd year:

• you will design a neural network based on the experimental results and evaluate the computational accuracy of your chip
• you will learn how to inject noises into the algorithm to emulate the interference induced by magnetic field and cryogenic temperatures, and develop error correction mitigation strategies.

Entry requirements:

You must have a UK 2:1 honours degree, or its international equivalent.

Fees and funding:

Full scholarships include tuition fees, a stipend at the UKRI rate plus 10% ORC enhancement tax-free per annum for up to 3.5 years (totalling £22,858 for 2025/26, rising annually) and a budget of £4200 for things like conference travel. UK, EU and Horizon Europe students are eligible for scholarships. Chinese Scholarship Council funded students are eligible for fee waivers. Funding for other international applicants is very limited and highly competitive. Overseas students who have secured or are seeking external funding are welcome to apply.

How to apply:

Closing date: 31 Dec 2026

Apply now

You need to:

• choose programme type (Research), 2026/27, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Electronic & Electrical Engineering (7092)
• add name of the supervisor in section 2 of the application

Applications should include:

• research proposal
• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (if applicable)