Sensor fusion enabled by photonic neuromorphic computing using phase-change materials

The use of functional materials that can "accumulate" information to carry out both memory and computing tasks in-situ is a growing field. Using optical integration onto silicon chips provides a unique opportunity to combine the benefits of silicon scaling and the wavelength multiplexing of optics onto a single platform. In this proposal, we will significantly expand our initial work on carrying out non-von Neumann computations (such as Vector-Matrix Multiplication directly in hardware) to a larger matrix to prove a lab scale demonstration of the potential for such hardware in defence-relevant computing tasks.

Phase-change devices, that exploit reversible cycling between amorphous and crystalline states in chalcogenide alloys such as Ge2Sb2Te5 (GST), have been with us for some time now, providing binary non-volatile memories in both the optical domain (e.g. DVD-RAM and Blu-Ray RW disks) and more recently the electrical domain (e.g. in the 3D-XPoint memory announced by Intel/Micron in 2015). Along with others, we have also recently shown that phase-change based devices can provide a number of important non-von Neumann computing/processing functionalities, namely:

1) By exploiting a multi-level memory mode of operation, phase-change devices can mimic the basic operation of a biological synapse.
2) By operating in an 'energy' accumulation mode, phase-change devices can mimic the basic operation of a biological neuron.
3) By exploiting the accumulation mode, phase-change devices can also provide a form of non-von Neumann computing in which memory and (arithmetic) processing are carried out simultaneously in the same device.

In recent work (Li et al, Optica 7 (3), 218-225), we have shown the quantitative experimental differences between Silicon and Silicon Nitride waveguides, and demonstrated multibit optical writing on a SOI platform. In this PhD proposal we will build on this firm foundation of proven basic principles and develop (design, fabricate and test) phase-change-based computing primitives, specifically synapse and neuron mimics and (binary and multi-level) non-volatile memories and combine such primitives into non-von Neumann computing networks (architectures). We will use these to demonstrate proof-of-concept for defence-relevant computational tasks related to sensor processing and management.

Student is IDS with company DSTL