FABRICATION AND STUDY OF THE p − Si/α − Si/Ag MEMRISTOR CROSSBAR ARRAY

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Аннотация

We study the formation of the conductive channels in α-Si memristors and demonstrate their operation in the crossbar array. The latter can be utilised as the basic component of the neuromorphic chip tailored for edge computing. The conductive channels in α-Si are formed by the migration of Ag along with Cu ions. Such a channel has switching current-voltage characteristics at high bias, Vbias > 2V, and highly non-linear that at low bias, Vbias < 0.5V. Memristor can be re-programmed to different resistance states with short voltage pulses of amplitude above 2 V. We demonstrate the programming of the memristor crossbar array and its operation in vector-by-matrix multiplication with an 87% accuracy.

Список литературы

  1. J. J. Yang, D. B. Strukov, and D. R. Stewart, Memristive Devices for Computing, Nature Nanotechnology 8, 13 (2012).
  2. Krestinskaya, A. P. James, and L. O. Chua, Neuromemristive Circuits for Edge Computing: A Review, IEEE Trans. on Neural Networks and Learning Systems 31, 4 (2020).
  3. D. Marković, A. Mizrahi, D. Querlioz, and J. Grollier, Physics for Neuromorphic Computing, Nature Rev. Phys. 2, 499 (2020).
  4. R. Yang, P. Gao, S. Gaba, et al., Observation of Conducting Filament Growth in Nanoscale Resistive Memories, Nature Commun. 3, 732 (2012).
  5. V. Emelyanov, K. .E. Nikiruy, V. A. Demin, et al., Yttria-Stabilized Zirconia Cross-Point Memristive Devices for Neuromorphic Applications, Microelectronic Engineering 215, 110988 (2019) 6. J. Woo and S. Yu, Resistive Memory-Based Analog Synapse: The Pursuit for Linear and Symmetric Weight Update, IEEE Nanotechnology Magazine 12, 36 (2018)
  6. Yeon, P. Lin, C. Choi, et al., Alloying Conducting Channels for Reliable Neuromorphic Computing, Nature Nanotechnology 15, 574 (2020).
  7. Д. В. Ичёткин, М. Е. Ширяев, Д. В. Новиков, и др., Многоуровневые мемристорные структуры на основе a-Si с повышенной устойчивостью резистивного переключения и малыми токами потребления, Письма в ЖТФ 49, 39 (2023).
  8. D. McBrayer, R. M. Swanson, T. W. Sigmon, Diffusion of Metals in Silicon Dioxide, J. Electrochem. Soc. 133, 1242 (1986).
  9. F. Rollert, N. A. Stolwijk, H. Mehrer, Solubility, Diffusion and Thermodynamic Properties of Silver in Silicon, J. Phys. D: Appl. Phys. 20, 1148 (1987).
  10. Z. Ma, J. Ge, W. Chen, et al., Reliable Memristor Based on Ultrathin Native Silicon Oxide, ACS Applied Materials and Interfaces 14, 21207 (2022).
  11. A. Istratov, E. R. Weber, Physics of Copper in Silicon, J. Electrochem. Soc. 149, G21 (2002).
  12. Ren, S. Liu, R. Cai, et al., Algorithm-Hardware Cooptimization of the Memristor-Based Framework for Solving Socp and Homogeneous Qcqp Problems, 2017 22nd Asia and South Pacific Design Automation Conference (ASPDAC), IEEE (2017).
  13. Xia and J. J. Yang, Memristive Crossbar Arrays for Brain-Inspired Computing, Nature Materials 18, 309 (2019).
  14. Yakopcic, T. M. Taha, G. Subramanyam, R. E. Pino, and S. Rogers, A Memristor Device Model, IEEE Electron Device Lett. 32, 1436 (2011).
  15. Konlechner, A. Allagui, V. N. Antonov, and D. Yudin, A Superstatistics Approach to the Modelling of Memristor Current–voltage Responses, Phys. A: Statistical Mechanics and its Applications 614, 128555 (2023).
  16. P. G. Le Comber and W. E. Spear, Electronic Transport in Amorphous Silicon Films, Phys. Rev. Lett. 25, 509 (1970).
  17. Joshi, and J. M. Acken, Sneak Path Characterization in Memristor Crossbar Circuits, Int. J. Electronics 108, 1255 (2020).

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