H. Das, R. D. Febbo, S. N. B. Tushar, N. N. Chakraborty, M. Liehr, N. C. Cady and G. S. Rose

August, 2023

IEEE Transactions on Circuits and Systems I: Regular Papers

https://ieeexplore.ieee.org/document/10214420

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Abstract

Memristors provide a tempting solution for weighted synapse connections in neuromorphic computing due to their size and non-volatile nature. However, memristors are unreliable in the commonly used voltage-pulse-based programming approaches and require precisely shaped pulses to avoid programming failure. In this paper, we demonstrate a current-limiting-based solution that provides a more predictable analog memory behavior when reading and writing memristive synapses. With our proposed design READ current can be optimized by ~19x compared to the 1T1R design. Moreover, our proposed design saves ~9x energy compared to the 1T1R design. Our 3T1R design also shows promising write operation which is less affected by the process variation in MOSFETs and the inherent stochastic behavior of memristors. Memristors used for testing are hafnium oxide based and were fabricated in a 65 nm hybrid CMOS-memristor process. The proposed design also shows linear characteristics between the voltage applied and the resulting resistance for the writing operation. The simulation and measured data show similar patterns with respect to voltage pulse based programming and current compliance based programming. We further observed the impact of this behavior on neuromorphic-specific applications such as a spiking neural network.

Citation Information

Text

author      H. Das and R. D. Febbo and S. N. B. Tushar and N. N. Chakraborty and M. Liehr and N. C. Cady and G. S. Rose
title       An Efficient and Accurate Memristive Memory for Array-Based Spiking Neural Networks
journal     IEEE Transactions on Circuits and Systems I: Regular Papers
volume      70
number      12
month       December
year        2023
pages       4804-4815
url         https://ieeexplore.ieee.org/document/10214420
doi         10.1109/TCSI.2023.3301020

Bibtex

@ARTICLE{dft:23:eam,
    author = "H. Das and R. D. Febbo and S. N. B. Tushar and N. N. Chakraborty and M. Liehr and N. C. Cady and G. S. Rose",
    title = "An Efficient and Accurate Memristive Memory for Array-Based Spiking Neural Networks",
    journal = "IEEE Transactions on Circuits and Systems I: Regular Papers",
    volume = "70",
    number = "12",
    month = "December",
    year = "2023",
    pages = "4804-4815",
    url = "https://ieeexplore.ieee.org/document/10214420",
    doi = "10.1109/TCSI.2023.3301020"
}