First-Principles Prediction of Potential Candidate Materials MCu3X4 ( M = V,Nb,Ta;X=S,Se,Te ) for Neuromorphic Computing

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Inspired by the neurosynaptic frameworks in the human brain, neuromorphic computing is expected to overcome the bottleneck of traditional von Neumann architecture and be used in artificial intelligence. Here, we predict a class of potential candidate materials, $M{\mathrm{Cu}}_{3}{X}_{4}$ ($M$ = $\mathrm{V}$, $\mathrm{Nb}$, $\mathrm{Ta};\phantom{\rule{0.2em}{0ex}}X\phantom{\rule{0.25em}{0ex}}=\phantom{\rule{0.25em}{0ex}}\mathrm{S},\phantom{\rule{0.2em}{0ex}}\mathrm{Se},\phantom{\rule{0.2em}{0ex}}\mathrm{Te}$), for neuromorphic computing applications through first-principles calculations based on density-functional theory. We find that when $M{\mathrm{Cu}}_{3}{X}_{4}$ are inserted with $\mathrm{Li}$ atom, the systems would transform from semiconductors to metals due to the considerable electron filling [approximately 0.8 electrons per formula unit (f.u.)] and still maintain well-structural stability. Meanwhile, the inserted $\mathrm{Li}$ atom also has a low diffusion barrier (approximately 0.6 eV/f.u.), which ensures the feasibility to control the insertion and extraction of $\mathrm{Li}$ by gate voltage. These results establish that the system can achieve the reversible switching between two stable memory states, i.e., high or low resistance state, indicating that it could potentially be used to design synaptic transistor to enable neuromorphic computing. Our work provides inspiration for advancing the search of candidate materials related to neuromorphic computing from the perspective of theoretical calculations.