DOI: https://doi.org/10.29363/nanoge.neuronics.2024.025
Publication date: 18th December 2023
In this talk, I will share our work on the ionic electrochemical synapses, whose electronic conductivity we control deterministically by electrochemical insertion/extraction of dopant ions into/out of the channel layer. This work is motivated by the need to enable significant reductions in the energy consumption of computing, and is inspired by the ionic processes in the brain. Proton as the working ion in our research presents with very low energy consumption, on par with biological synapses in the brain. Our modeling results indicate the desirable material properties, such as ion conductivity and interface charge transfer kinetics, that we must achieve for fast (ns), low energy (< fJ) and low voltage (<1V) performance of these devices. Importantly, the conductance change in these electrochemical devices depends non-linearly on the gate voltage, due to field-enhanced ion migration in the electrolyte, and charge transfer kinetics at the electrolyte-channel interface. We are leveraging these intrinsic nonlinearities to emulate bio-realistic learning rules deduced from neuroscience studies, such as spike timing dependence of plasticity and Hebbian learning rules. Our findings provide pathways towards brain-inspired hardware that has high yield and consistency and uses significantly lesser energy as compared to current computing architectures.
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