Materials for Neuromorphic Devices

The emerging paradigm of neuromorphic computing is inspired by neural networks of the brain and based on energy-efficient hardware for information processing. To create devices that mimic what occurs in our brains’ neurons and synapses, the scientific community must overcome a fundamental molecular engineering challenge: how to design devices that exhibit controllable and energy-efficient transition between different resistive states triggered by incoming stimuli. Our work in this area is supported by the Quantum Materials for Energy Efficient Neuromorphic Computing (QMEENC) research center, an Energy Frontier Research Center headquartered at UC San Diego.

Transition Metals Oxides for Neuromorphic Applications

Several transition metal oxides (TMOs) have been proposed as promising resistive switching materials, i.e. systems showing tunable resistance states, in the presence of an external electrical bias. These TMOs exhibit a metal-to-insulator transition (MIT) as a function of pressure, temperature, or doping, which may be designed to mimic the behavior of neurons and synapses in the presence of stimuli. We study the properties of promising TMOs for neuromorphic applications using first principles, electronic structure calculations, and we develop models to predict the electric bias and the effect of structural distortions in driving the MIT.