Bidirectional electric-induced conductance based on gete/sb2 te3 interfacial phase change memory for neuro-inspired computing

Shin Young Kang, Soo Min Jin, Ju Young Lee, Dae Seong Woo, Tae Hun Shim, In Ho Nam, Jea Gun Park, Yuji Sutou, Yun Heub Song

Research output: Contribution to journalArticlepeer-review


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/Sb2 Te3)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/Sb2 Te3 superlattice structure, multiple conductance states were implemented by applying the appropriate electrical pulses. Furthermore, we found that the bidirectional switching behavior of a (GeTe/Sb2 Te3)16 iPCM can achieve a desired resistance level by using the pulse width. Therefore, we fabricated a Ge2 Sb2 Te5 PCM and designed a pulse scheme, which was based on the phase transition mechanism, to compare to the (GeTe/Sb2 Te3)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/Sb2 Te3)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.

Original languageEnglish
Article number2692
JournalElectronics (Switzerland)
Issue number21
Publication statusPublished - 2021 Nov 1


  • Artificial synaptic device
  • Interfacial phase change memory
  • Neuromorphic devices
  • Phase change memory
  • Superlattice

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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