Memristive crossbar arrays(MCAs)offer parallel data storage and processing for energy-efficient neuromorphic com-puting.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℃.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 elec-tronics.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.
Division of Solid-State Electronics,Department of Electrical Engineering,Uppsala University,75121 Uppsala,Sweden
Department of Chemistry,Uppsala University,Uppsala,Sweden
Department of Physics and Astronomy,Uppsala University,Uppsala,Sweden
State Key Laboratory of High Performance Ceramics and Superfine Microstructure,Shanghai Institute of Ceramics,Chinese Academy of Sciences,Shanghai 200050,People's Republic of China
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