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Synaptic Devices Based on Phase-Change Memory

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Neuro-inspired Computing Using Resistive Synaptic Devices

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Abstract

The biological brain has the capability of learning, pattern recognition, processing imprecisely defined data, and executing complex computational tasks. Consisting of 1011 neurons and 1015 synapses as the major computational components, the biological brain is extremely power efficient, massively parallel, structurally plastic, and exceptionally robust against noise and variations (Kuzum et al. Nanotechnology 24:382001, 2013). The question of how to design and build a compact neuromorphic system is a grand challenge for academia and industry. An electronic synaptic device is an essential element in such neuromorphic systems. Among various electronic synapses candidates, nonvolatile memory-based synaptic devices have the highest potential to realize massive parallelism and 3D integration for achieving high function per unit volume. This chapter will focus on synaptic devices based on phase-change memory (PCM). We first review the basics of phase-change synaptic devices: device operation, phase-change materials, conduction mechanism, power consumption, and scaling. We then review the use of PCM synaptic device implementations spanning from single device operation to various array architecture designs in the following sections. The concept of spike-timing-dependent plasticity (STDP), various pulse scheme designs, and pulse programming techniques for plasticity will be explained and compared. Last, we will discuss recent advances in designing PCM synaptic device to achieve lower power consumption and more stable resistance states.

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Acknowledgments

DK acknowledges the Office of Naval Research Young Investigator Award. SWF and HSPW are supported in part by the member companies of the Stanford Non-Volatile Memory Technology Research Initiative (NMTRI), member companies of the Stanford SystemX Alliance, and the National Science Foundation (Grant #DGE-4747 and Expeditions in Computing [Award # 1317560]).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA

    Yuhan Shi & Duygu Kuzum

  2. Department of Electrical Engineering, Stanford University, 420 Via Palou, Stanford, CA, 94305, USA

    Scott Fong & H.-S. Philip Wong

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  1. Yuhan Shi
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  2. Scott Fong
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Correspondence to H.-S. Philip Wong or Duygu Kuzum .

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  1. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona, USA

    Shimeng Yu

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Shi, Y., Fong, S., Wong, HS.P., Kuzum, D. (2017). Synaptic Devices Based on Phase-Change Memory. In: Yu, S. (eds) Neuro-inspired Computing Using Resistive Synaptic Devices. Springer, Cham. https://doi.org/10.1007/978-3-319-54313-0_2

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