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Grain boundary control for high-reliability HfO2-based RRAM

Author

Listed:
  • Jeong, Dong Geun
  • Park, Eunpyo
  • Jo, Yooyeon
  • Yang, Eunyeong
  • Noh, Gichang
  • Lee, Dae Kyu
  • Kim, Min Jee
  • Jeong, YeonJoo
  • Jang, Hyun Jae
  • Joe, Daniel J.
  • Chang, Jiwon
  • Kwak, Joon Young

Abstract

Recently, neuromorphic computing has emerged as a promising solution to the limitations of conventional von Neumann computing architectures. Two-terminal memristors, particularly resistive random-access memory (RRAM), are gaining attention because of their structural resemblance to biological synapses, enabling the emulation of neuromorphic synaptic operations. Metal oxide-based RRAM leverages the formation and rupture of conductive filaments based on oxygen vacancies for resistive switching. Despite extensive research on conductive filament formation in amorphous and crystalline configurations, understanding of the impact of grain sizes and boundaries on RRAM properties remains limited. In this study, we investigate the influence of grain conditions on addressing challenges such as high operating voltages and large resistance variations during switching operations using a Ti/HfO2/Pt structure. Additionally, this study extends the application of HfO2-based RRAM to neuromorphic computing, demonstrating linear synaptic weight updates, which are essential for constructing accurate neuromorphic systems. Our device has better reliability than amorphous HfO2-based RRAM, which we achieve by precisely manipulating grain sizes and boundaries depending on the annealing conditions to solve cycle-to-cycle and device-to-device variations. Our experimental results suggest the importance of precise grain control for fabricating highly reliable and robust RRAM and artificial synaptic devices.

Suggested Citation

  • Jeong, Dong Geun & Park, Eunpyo & Jo, Yooyeon & Yang, Eunyeong & Noh, Gichang & Lee, Dae Kyu & Kim, Min Jee & Jeong, YeonJoo & Jang, Hyun Jae & Joe, Daniel J. & Chang, Jiwon & Kwak, Joon Young, 2024. "Grain boundary control for high-reliability HfO2-based RRAM," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:chsofr:v:183:y:2024:i:c:s0960077924005083
    DOI: 10.1016/j.chaos.2024.114956
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    References listed on IDEAS

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    1. Kaushik Roy & Akhilesh Jaiswal & Priyadarshini Panda, 2019. "Towards spike-based machine intelligence with neuromorphic computing," Nature, Nature, vol. 575(7784), pages 607-617, November.
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    3. Park, Jinwoo & Kim, Tae-Hyeon & Kim, Sungjoon & Lee, Geun Ho & Nili, Hussein & Kim, Hyungjin, 2021. "Conduction mechanism effect on physical unclonable function using Al2O3/TiOX memristors," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
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