Ag2S-Based Flexible Memristors for Neuromorphic Computing

Abstract: Memristive crossbar arrays hold the great promise for fast and energy efficient neuromorphic computing due to their parallel data storage and processing capabilities. As the key component, memristor should achieve stable resistance switching (RS) characteristics with low energy inputs and be compatible with complementary metal–oxide–semiconductor (CMOS) technology. It should also exhibit sufficient device flexibility for applications in wearable electronics. In this thesis, we fabricate flexible memristors (FMs) based on Ag2S films, investigate their RS behavior and mechanism, demonstrate CMOS-compatible array integration and validate their computing applications.The thesis starts with a full-inorganic FM, utilizing ductile Ag2S thick films as both a flexible substrate and a functional electrolyte. The device exhibits dense multiple-level non-volatile states with a remarkable ON/OFF ratio of 106. The exceptional RS behavior is induced by sequential processes of Schottky barrier height (SBH) modification at the contact interface and silver filament formation inside the electrolyte. As a follow-up, we show that interface RS by SBH modification can be facilitated with smaller setting voltages. In contrast to traditional filamentary memristors, the sole interface RS achieves an ultralow switching energy of ~0.2 fJ. An image processing with interface RS indeed exhibits 2 orders of magnitude lower power than that with filamentary RS on the same hardware.Moreover, interface RS avoids the  stochastic nature of filament formation and ablation inside electrolytes. The Ag2S-based FM operating with interface RS exhibits an impressive cycle-to-cycle variation of 1.4%, which is in direct contrast to the variation (28.9%) of filament RS extracted from the same device. Its significantly improved image learning ability over filament RS is also demonstrated during the frequent weight update process in simulations.Large-scale memristor array, with energy-efficient memristive units at each cross-point, is imperative for neuromorphic computing. We further demonstrate a wafer-scale integration of Ag2S-based memristive crossbar array by fully CMOS-compatible processes. With modulated Ag2S microstructure, the integrated memristors exhibit a record low threshold voltage of approximately -0.1V for filament formation, and an ultra-small switching energy approaching biological synapses at femtojoules. In addition, the same crossbar arrays are integrated on flexible polyimide substrates, on which analogue multiply accumulate calculations and image recognition simulations are successfully demonstrated. An impressive accuracy of 92.6% is finally achieved in handwritten digit recognition, with the intrinsic nonidealities of the integrated memristors compensated by an advanced training algorithm.