Abstract: Memristive crossbar arrays (MCAs) offer parallel data storage and processing for energy-efficient neuromorphic computing. However, most wafer-scale MCAs that are compatible with complementary metal-oxide-semiconductor (CMOS) technology still suffer from substantially larger energy consumption than biological synapses, due to the slow kinetics of forming conductive paths inside the memristive units. Here we report wafer-scale Ag2S-based MCAs realized using CMOS-compatible processes at temperatures below 160 °C. Ag2S electrolytes supply highly mobile Ag+ ions, and provide the Ag/Ag2S interface with low silver nucleation barrier to form silver filaments at low energy costs. By further enhancing Ag+ migration in Ag2S electrolytes via microstructure modulation, the integrated memristors exhibit a record low threshold of approximately − 0.1 V, and demonstrate ultra-low switching-energies reaching femtojoule values as observed in biological synapses. The low-temperature process also enables MCA integration on polyimide substrates for applications in flexible electronics. Moreover, the intrinsic nonidealities of the memristive units for deep learning can be compensated by employing an advanced training algorithm. An impressive accuracy of 92.6% in image recognition simulations is demonstrated with the MCAs after the compensation. The demonstrated MCAs provide a promising device option for neuromorphic computing with ultra-high energy-efficiency.
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