Layered metal dichalcogenides (LMDs) neuromorphic memristor devices offer a promising alternative toconventional von Neumann architectures, addressing speedand energy efficiency constraints. However, challenges remain...Layered metal dichalcogenides (LMDs) neuromorphic memristor devices offer a promising alternative toconventional von Neumann architectures, addressing speedand energy efficiency constraints. However, challenges remainin controlling resistive switching and operating voltage incrystalline LMD memristors due to environmental stabilization issues, which hinder neural network hardware development. Herein, we introduce an optimization method formemristor operation by controlling oxidation through ozonetreatment, creating a SnO_(x)/SnS_(2) resistive layer. These optimized memristors demonstrate low switching voltages (~1 V),rapid switching speeds (~20 ns), high switching ratios (10^(2)),and the ability to emulate synaptic weight plasticity. Crosssectional transmission electron microscopy and energy-dispersive X-ray spectroscopy identified defects and Ti conductive filaments in the resistive switching layer, contributingto uniform switching and minimized operating variation. Thedevice achieved 90% accuracy in MNIST handwritten recognition, and hardware-based image convolution was successfully implemented, showcasing the potential of SnO_(x)/SnS_(2)memristors for neuromorphic applications.展开更多
基金supported by the National Natural Science Foundation of China (22175060 and 12304082)Shenzhen Science and Technology Program (JCYJ20220530160407016)+1 种基金the Natural Science Foundation of Hunan Province (2023JJ20001)the support from the U.S. National Science Foundation (2004251)。
文摘Layered metal dichalcogenides (LMDs) neuromorphic memristor devices offer a promising alternative toconventional von Neumann architectures, addressing speedand energy efficiency constraints. However, challenges remainin controlling resistive switching and operating voltage incrystalline LMD memristors due to environmental stabilization issues, which hinder neural network hardware development. Herein, we introduce an optimization method formemristor operation by controlling oxidation through ozonetreatment, creating a SnO_(x)/SnS_(2) resistive layer. These optimized memristors demonstrate low switching voltages (~1 V),rapid switching speeds (~20 ns), high switching ratios (10^(2)),and the ability to emulate synaptic weight plasticity. Crosssectional transmission electron microscopy and energy-dispersive X-ray spectroscopy identified defects and Ti conductive filaments in the resistive switching layer, contributingto uniform switching and minimized operating variation. Thedevice achieved 90% accuracy in MNIST handwritten recognition, and hardware-based image convolution was successfully implemented, showcasing the potential of SnO_(x)/SnS_(2)memristors for neuromorphic applications.
