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Abstract
The development of the resistive switching cross-point array as the next-generation platform for high-density storage, in-memory computing and neuromorphic computing heavily relies on the improvement of the two component devices, volatile selector and nonvolatile memory, which have distinct operating current requirements. The perennial current-volatility dilemma that has been widely faced in various device implementations remains a major bottleneck. Here, we show that the device based on electrochemically active, low-thermal conductivity and low-melting temperature semiconducting tellurium filament can solve this dilemma, being able to function as either selector or memory in respective desired current ranges. Furthermore, we demonstrate one-selector-one-resistor behavior in a tandem of two identical Te-based devices, indicating the potential of Te-based device as a universal array building block. These nonconventional phenomena can be understood from a combination of unique electrical-thermal properties in Te. Preliminary device optimization efforts also indicate large and unique design space for Te-based resistive switching devices.
Resistive switching devices have great promise for a wide variety of technological applications. Here, Yang et al demonstrate that electrochemically induced tellurium filament can give rise to resistive switching, and show that devices based on this can provide a number of advantages compared to metallic filament-based devices.
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1 Tsinghua University, Department of Precision Instrument, Center for Brain Inspired Computing Research, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178)
2 Fudan University, Frontier Institute of Chip and System, Shanghai, China (GRID:grid.8547.e) (ISNI:0000 0001 0125 2443); Chinese Academy of Sciences, State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-System and Information Technology, Shanghai, China (GRID:grid.9227.e) (ISNI:0000000119573309)
3 Tsinghua University, State Key Laboratory of New Ceramics and Fine Processing, School of Materials Science and Engineering, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178)
4 Beijing Information Science & Technology University, School of Instrument Science and Opto Electronics Engineering, Beijing, China (GRID:grid.443248.d) (ISNI:0000 0004 0467 2584)
5 Tsinghua University, State Key Laboratory of Tribology, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178)
6 Swansea University, College of Engineering, Swansea, UK (GRID:grid.4827.9) (ISNI:0000 0001 0658 8800)
7 Southwest University, School of Electronic and Information Engineering, Chongqing, China (GRID:grid.263906.8) (ISNI:0000 0001 0362 4044)
8 Southwest University, School of Artificial Intelligence, Chongqing, China (GRID:grid.263906.8) (ISNI:0000 0001 0362 4044)
9 Chinese Academy of Sciences, State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Micro-System and Information Technology, Shanghai, China (GRID:grid.9227.e) (ISNI:0000000119573309)
10 Tsinghua University, Department of Precision Instrument, Center for Brain Inspired Computing Research, Beijing, China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178); Chinese Institute for Brain Research, Beijing, China (GRID:grid.510934.a)