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(CMO...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 Ag_(2)S-based MCAs realized using CMOS-compatible processes at temperatures below 160℃.Ag_(2)S electrolytes supply highly mobile Ag+ions,and provide the Ag/Ag_(2)S interface with low silver nucleation barrier to form silver filaments at low energy costs.By further enhancing Ag+migration in Ag_(2)S 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.展开更多
基金supported by the Swedish Strategic Research Foundation(SSF FFL15-0174 to Zhen Zhang)the Swedish Research Council(VR 2018-06030 and 2019-04690 to Zhen Zhang)+1 种基金the Wallenberg Academy Fellow Extension Program(KAW 2020-0190 to Zhen Zhang)the Olle Engkvist Foundation(Postdoc grant 214-0322 to Zhen Zhang).
文摘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 Ag_(2)S-based MCAs realized using CMOS-compatible processes at temperatures below 160℃.Ag_(2)S electrolytes supply highly mobile Ag+ions,and provide the Ag/Ag_(2)S interface with low silver nucleation barrier to form silver filaments at low energy costs.By further enhancing Ag+migration in Ag_(2)S 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.
