A Self-Formed Ag Nanostructure Based Neuromorphic Device Performing Arithmetic Computation and Area Integration:...

A material equivalent of a biosynapse is the key to neuromorphic architecture. Here we report a self-forming labyrinthine Ag nanostructure activated with a few pulses of 0.5 V, width and interval set at 50 ms, at current compliance (I_CC) of 400 nA, serving as the active material for a highly stable device with programmable volatility. Both the conductance (G) and its retention time (t_r) in the potentiated state are found to vary linearly with the pulse number for pulses of positive and negative polarities, with the nonlinearity factors being noticeably small, ∼0.03 for G during potentiation and ∼0.08 during depression. This was tested for over 200 days, and the results were highly reproducible. Relying on the high linearity, arithmetic operations involving counting of positive and negative integers were realized using pulses of both polarities, often by mixing them in the feeding sequence. The observed outcomes based on G and independently from t_r are highly accurate, with deviations being typically less than ∼1.5% from the expected results. Notably, the way the pulse polarities are mixed is found to have an influence, with random sequences producing relatively larger deviations in integer estimation. However, deviations decreased with higher I_CC values, which promoted stronger filament formation in the percolation networks. Besides, the G and t_r values were found to vary with the pulse amplitude as well, which enabled the calculation of the area under a curve. Further, the device exhibited a simulation-based image classification accuracy of 94.95%, close to the ideal value (96.05%). Simulations utilizing the finite element method have showcased the uniqueness of the labyrinthine morphology, giving rise to field intensification along potential percolative paths.