Mott Materials for Artificial Intelligence – MoMA

Artificial intelligent (AI) systems are being developed at a vertiginous pace. This progress follows one of two roads: either
deep neural network algorithms running in conventional supercomputers, or building dedicated microchips with conventional
electronics to implement the neurons and synapses of the AI networks. However, the human brain has about 10^11 neurons
connected by 10^15 synapses, a volume of one litre and needs just about ten watts. That degree of interconnection and
power efficiency cannot be achieved with silicon electronics. This issue calls for a disruptive technology: to invent and
interconnect energetically efficient ‘neuromorphic’ electronic devices. They will allow to build --directly on hardware-- the AI
neural networks for neuromorphic computation. The implementation of artificial synapses has already been achieved with
memristors, exploiting the physical phenomenon of Non-volatile Resistive Switching. In contrast, implementing artificial
neurons remains a big challenge. In a recent development, the PI and collaborators showed that such artificial neurons could
be made with quantum materials known as Mott insulators [Patent No.US 2017/0124449A1]. The key observation was that a
train of electric pulses (spikes) may collapse the resistance and produce a current spike through the device, analogous to
the emission of an action potential in neurons. However, the control of these materials under strong electric pulses is difficult
and remains not understood. This is preventing further progress in this field. The goal of the present project is to achieve
decisive theoretical understanding of the resistive collapse of Mott insulators, provide key guidance to permit experimental
progress and demonstrate how these devices may be interconnected to implement artificial neuron circuits with
neuromorphic functionalities. The project shall provide a stepping-stone for the new electronics needed to build the
Neuromorphic Computers of the 21st Century.