Oxide film devices for AI processing of temporal signals

It is possible to run machine learning algorithms to classify and predict time-dependent input signals from sensors in application areas such as language processing, environmental, engineering, or medical monitoring. However, high energy consumption associated with the state-of-the-art hardware for neural networks is hindering development of mobile, compact sensors that can be operated stand-alone and offline. This is an opportunity to design devices that use fundamentally new physical approaches to processing neural networks in order to tackle this issue.

The goal of this project is experimental development of thin film devices capable of processing time-dependent electrical signals as part of a neural network. The project includes preparation of thin films of oxide materials; application of different characterisation techniques to study the material properties; designing novel electronic devices; testing their performance with respect to the industry standard benchmarks. This research project is at the intersection of artificial intelligence and device physics, and involves collaborative work between academic researchers in Physics, Chemistry, Computer Science and is linked to the EPSRC grants EP/T027479/1 and EP/S032843/1.