Room-Temperature Fabricated Multilevel Nonvolatile Lead-Free Cesium Halide Memristors for Reconfigurable In-Memory Computing

Recently, conductive-bridging memristors based on metal halides, such as halide perovskites, have been demonstrated as promising components for brain-inspired hardware-based neuromorphic computing. However, realizing devices that simultaneously fulfill all of the key merits (low operating voltage, high dynamic range, multilevel non-volatile storage capability, and good endurance) remains a great challenge. Herein, we describe lead-free cesium halide memristors incorporating a MoOX interfacial layer as a type of conductive-bridging memristor. With this design, we obtained highly uniform and reproducible memristors that exhibited all-around resistive switching characteristics: ultralow operating voltages (< 0.18 V), low variations (< 30 mV), long retention times (> 106 s), high endurance (> 105, full on/off cycles), record-high on/off ratios (> 10(10), smaller devices having areas < 5 x 10(-4) mm2), fast switching (< 200 ns), and multilevel programming abilities (> 64 states). With these memristors, we successfully implemented stateful logic functions in a reconfigurable architecture and accomplished a high classification accuracy (ca. 90%) in the simulated hand-written-digits classification task, suggesting their versatility in future in-memory computing applications. In addition, we exploited the room-temperature fabrication of the devices to construct a fully functional three-dimensional stack of memristors, which demonstrates their potential of high-density integration desired for data-intensive neuromorphic computing. High-performance, environmentally friendly cesium halide memristors provide opportunities toward next-generation electronics beyond von Neumann architectures.

Automated summary

This paper investigates lead-free cesium halide memristors that exhibit low operating voltage, high dynamic range, multilevel non-volatile storage capability, and good endurance, making them suitable for brain-inspired hardware-based neuromorphic computing.