Development of New Materials for Optically Programmable and Re-Programmable Printable Memristors for Neuromorphic Computing

Printed electronics (i.e. the technology of fabricating electronic devices and circuits using additive, low-temperature printing processes) promises low-cost, on-demand electronic circuit fabrication, in new form-factors (e.g. on flexible or conformal substrates, using biocompatible components, over large areas). The manufacture of printed electronic systems requires much simpler and cheaper hardware solutions than those needed for making traditional integrated circuits and some of the most promising commercial opportunities in the Internet of Things are in intelligent sensors, smart tags and labels, and other 'edge computing' scenarios, where intelligent processing is embedded in the low-cost device itself not transferred to the cloud. Delivery of these systems requires the low-power, low-cost, and low component count of analogue computing, combined with deep learning algorithms and this has led to a resurgence of interest in analogue neural network computing, sometimes termed a "neuromorphic" approach to computation. Critical components of the hardware needed to implement these machine learning algorithms require new materials for robust, programmable, printed devices. In this PhD project new materials and formulations will be developed to deliver arrays and circuits in which the materials resistance is capable of being programmed after fabrication.
This will be initially achieved by optical encoding of materials resistance to decouple the write and read processes in memristor devices to deliver stable, robust and reproducible programmable arrays and hence circuits (e.g. for the synaptic weights of neural networks). This approach will be extended to develop materials for optically re-programmable arrays in which illumination at different wavelengths will be used to repeatedly encode and erase the resistance of device arrays. Finally the research programme will investigate approaches to move from 2D planar printed arrays to 3D folded or printed structures with a greater device density and a more highly interconnected circuit architecture such as that seen in the human cortex.