Scalable nanocomposite parylene-based memristors: Multifilamentary resistive switching and neuromorphic applications

Memristors are promising candidates for synapse emulation in brain -inspired neuromorphic computing systems. The main obstacle to their usage in such systems is high variability of memristive characteristics and its severe negative effect on the neural network training. This paper addresses the issue from two points of view on the example of the parylene-based memristors: (i) the methods of the memristor internal stochasticity decrease and (ii) the methods of the memristive neural network architecture simplification. The introduction of an optimal Ag nanoparticle concentration (3 vol.%-6 vol.%) to the memristive structure leads to a statistically significant decrease in the switching voltage variation and endurance increase. Moreover, it is shown that post-fabrication annealing improves memristive characteristics, e.g., resistive switching window increases by an order of magnitude and exceeds 10(6), the switching voltage variation decreases by a factor of 2 (down to 7% for the set and 17% for the reset voltage), and thermostability is improved. Additional transmission electron microscopy and impedance spectroscopy analysis allowed establishing a multifilamentary resistive switching mechanism for nanocomposite parylene-based memristors. The simulation of the formal neural network based on these memristors demonstrates high classification accuracy with low variation for an important biomedical task, heart disease prediction, after careful feature selection and network architecture simplification. Future prospects of the controlled incorporation of the nanocomposite parylene-based memristors in neural networks are brightened by their scaling possibility in crossbar geometry.

Automated summary

This paper addresses the issue of high variability of memristive characteristics in brain-inspired neuromorphic computing systems and proposes methods to decrease the stochasticity of memristors and simplify the neural network architecture. Experiments show that optimizing the nanocomposite structure and performing post-fabrication annealing can improve the performance of memristors. Simulations demonstrate that neural networks based on these memristors have high classification accuracy and low variation in heart disease prediction. The controlled incorporation of nanocomposite memristors in neural networks shows promising prospects.