Synaptic Characteristics of Amorphous Boron Nitride-Based Memristors on a Highly Doped Silicon Substrate for Neuromorphic Engineering

Jinju Lee, Ji Ho Ryu, Boram Kim, Fayyaz Hussain, Chandreswar Mahata, Eunjin Sim, Muhammad Ismail, Yawar Abbas, Haider Abbas, Dong Keun Lee, Min Hwi Kim, Yoon Kim, Changhwan Choi, Byung Gook Park, Sungjun Kim

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


In this study, the resistive switching and synaptic properties of a complementary metal-oxide semiconductor-compatible Ti/a-BN/Si device are investigated for neuromorphic systems. A gradual change in resistance is observed in a positive SET operation in which Ti diffusion is involved in the conducting path. This operation is extremely suitable for synaptic devices in hardware-based neuromorphic systems. The isosurface charge density plots and experimental results confirm that boron vacancies can help generate a conducting path, whereas the conducting path generated by a Ti cation from interdiffusion forms is limited. A negative SET operation causes a considerable decrease in the formation energy of only boron vacancies, thereby increasing the conductivity in the low-resistance state, which may be related to RESET failure and poor endurance. The pulse transient characteristics, potentiation and depression characteristics, and good retention property of eight multilevel cells also indicate that the positive SET operation is more suitable for a synaptic device owing to the gradual modulation of conductance. Moreover, pattern recognition accuracy is examined by considering the conductance values of the measured data in the Ti/a-BN/Si device as the synaptic part of a neural network. The linear and symmetric synaptic weight update in a positive SET operation with an incremental voltage pulse scheme ensures higher pattern recognition accuracy.

Original languageEnglish
Pages (from-to)33908-33916
Number of pages9
JournalACS applied materials & interfaces
Issue number30
StatePublished - 29 Jul 2020


  • boron nitride
  • density function theory
  • neural network
  • resistive switching
  • synaptic device


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