J. Mater. Sci. Technol. ›› 2021, Vol. 60: 21-26.DOI: 10.1016/j.jmst.2020.04.059

• Research Article • Previous Articles     Next Articles

Forming-free flexible memristor with multilevel storage for neuromorphic computing by full PVD technique

Tian-Yu Wang, Jia-Lin Meng, Qing-Xuan Li, Lin Chen*(), Hao Zhu, Qing-Qing Sun*(), Shi-Jin Ding, David Wei Zhang   

  1. State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China
  • Received:2020-03-18 Revised:2020-04-21 Accepted:2020-04-23 Published:2021-01-10 Online:2021-01-22
  • Contact: Lin Chen,Qing-Qing Sun

Abstract:

Flexible resistive random access memory (RRAM) has shown great potential in wearable electronics. With tunable multilevel resistance states, flexible memristors could be used to mimic the bio-synapses for constructing high-efficient wearable neuromorphic computing system. However, the flexible substrate has intrinsic disadvantages including low-temperature tolerance and poor complementary metal-oxide-semiconductor (CMOS) compatibility, which limit the development of flexible electronics. The physical vapor deposition (PVD) fabrication process could prepare RRAM without requirement of further treatment, which greatly simplified preparation steps and reduced the production costs. On the other hand, forming process, as a common pre-programing operation in RRAM, increases the energy consumption and limits the application scenarios of RRAM. Here, a NiO-based forming-free RRAM with low set voltage was fabricated via full PVD technique. The flexible device exhibited reliable resistive switching characteristics under flat state even compressive and tensile states (R = 10 mm). The tunable multilevel resistance states (5 levels) could be obtained by controlling the compliance current. Besides, synaptic plasticities also were verified in this device. The flexible NiO-based RRAM shows great potential in wearable forming-free multibit memory and neuromorphic computing electronics.

Key words: Full PVD process, Flexible memristor, Forming-free, Multilevel storage, Neuromorphic application