1. Bidirectional Electric-induced Conductance based on GeTe/Sb2Te3 Interfacial Phase Change Memory for Neuro-inspired Computing
- Author
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Jea-Gun Park, Tae Hun Shim, Yun-Heub Song, Dae Seong Woo, Shin Young Kang, Juyoung Lee, Soo Min Jin, In-Ho Nam, and Yuji Sutou
- Subjects
artificial synaptic device ,Phase transition ,Materials science ,TK7800-8360 ,Computer Networks and Communications ,Pulse (signal processing) ,business.industry ,Superlattice ,superlattice ,Conductance ,Phase-change memory ,phase change memory ,Nonlinear system ,interfacial phase change memory ,Hardware and Architecture ,Control and Systems Engineering ,Modulation ,Signal Processing ,electrical_electronic_engineering ,Optoelectronics ,neuromorphic devices ,Electronics ,Electrical and Electronic Engineering ,business ,Pulse-width modulation - Abstract
Corresponding to the principles of biological synapses, an essential prerequisite for hardware neural networks using electronics devices is the continuous regulation of conductance. We implemented artificial synaptic characteristics in a (GeTe/Sb2Te3)16 iPCM with a superlattice structure under optimized identical pulse trains. By atomically controlling the Ge switch in the phase transition that appears in the GeTe/Sb2Te3 superlattice structure, multiple conductance states were implemented by applying the appropriate electrical pulses. Furthermore, we found that the bidirectional switching behavior of a (GeTe/Sb2Te3)16 iPCM can achieve a desired resistance level by using the pulse width. Therefore, we fabricated a Ge2Sb2Te5 PCM and designed a pulse scheme, which was based on the phase transition mechanism, to compare to the (GeTe/Sb2Te3)16 iPCM. We also designed an identical pulse scheme that implements both linear and symmetrical LTP and LTD, based on the iPCM mechanism. As a result, the (GeTe/Sb2Te3)16 iPCM showed relatively excellent synaptic characteristics by implementing a gradual conductance modulation, a nonlinearity value of 0.32, and 40 LTP/LTD conductance states by using identical pulse trains. Our results demonstrate the general applicability of the artificial synaptic device for potential use in neuro-inspired computing and next-generation, non-volatile memory.
- Published
- 2021