Optogenetics-inspired photoelectric memories based on flexible nanogap electrodes

The aim of this project is to develop a new form of neuromorphic systems that merge photonic, electronic and ionic effects, bringing new prospects for in-memory computing and artificial visual memory applications. This will be achieved upon developing photoelectric memories that employ coplanar nanogap electrodes and multi-functional solution-processed materials, fabricated with low-cost processes compatible with large-area flexible substrates.

Neuromorphic engineering is poised to revolutionise information technologies by developing electronic devices that can realistically emulate biological neural networks. A key component is the "artificial synapse" that needs to be highly scalable and power efficient, whilst supporting rich dynamical responses akin to biological synapses. An emerging application of such platforms is in neuromorphic vision, where light sensors mimic the spatio-temporal nature of human vision not only by turning light into electrical signals but also by capturing and sending the useful-only information to the processing unit in an extremely efficient manner.

This is particularly relevant for real-time pattern recognition tasks that support a plethora of applications, from autonomous locomotion to point-of-care diagnostics, leveraging the sensors advances in speed, greater dynamic range and decreased computational cost. The field of optogenetics has pioneered the use of light-sensitive proteins that can be activated at will upon illumination and stimulate the neurons to fire.

Inspired by this technology, I will fabricate artificial synapses that can be controlled by optical stimuli, which, in contrast to electrical ones, can be spatially confined reducing thus significantly the crosstalk and noise, while they enable higher sensitivity and signal propagation speed. I will employ a simple nanofabrication method to design prototype devices of the same dimensionality as the actual synapse, namely large aspect ratio nanogap-separated electrodes, the nanogap being in the range of 15 nm, similar to the size of the synaptic cleft.

Interconnected nanogap electrodes emulating neuronal networks will be fabricated using adhesion lithography technique to address the current challenge of reliable manufacturing of nanoscale structures on large area flexible substrates. Finally, I will employ photosensitive polyoxometalate and halide perovskite to fabricate synaptic-like metal/semiconductor/metal junctions.

The film forming properties of these materials and their interfaces with the metal structures will be tailored to demonstrate neuromorphic functionalities, such as (a) associative learning, (b) parallel addressing of devices to emulate homeostasis of biological networks and (c) spatial integration of the optical stimulus in the array to enable selective storage depending on the light intensity/wavelength on each pixel.

My approach presents several advantages over the existing memristive technologies, which are based on crossbar architectures and solely electrical stimulus. First, coplanar nanogap electrodes, owing to their low dimensionality, hold great promise for achieving low power consumption and fast switching speeds, as already demonstrated with other types of devices (radiofrequency diodes, photodetectors), while their planar geometry facilitates a light-controlled operation, enabling both analogue tuning of resistance states and elimination of sneak currents in the array configuration.

Second, the aforementioned solution-processable materials present many attractive optoelectronic properties, chemical tunability and manufacturability merits that render them suitable to reach the set performance goals.

Successful implementation of this fellowship will represent a paradigm shift in the fabrication of neuromorphic devices, supporting the UK-based electronics and manufacturing industry, while it will establish me as a leader in the field of nanoscale optoelectronics for AI hardware.