On-chip phase-change photonic memory and computing

Zengguang Cheng; Carlos Ríos; Nathan Youngblood; C. David Wright; Wolfram H. P. Pernice; Harish Bhaskaran

doi:10.1117/12.2272127

24 August 2017 On-chip phase-change photonic memory and computing

Zengguang Cheng, Carlos Ríos, Nathan Youngblood, C. David Wright, Wolfram H. P. Pernice, Harish Bhaskaran

Proceedings Volume 10345, Active Photonic Platforms IX; 1034519 (2017) https://doi.org/10.1117/12.2272127
Event: SPIE Nanoscience + Engineering, 2017, San Diego, California, United States

Abstract

The use of photonics in computing is a hot topic of interest, driven by the need for ever-increasing speed along with reduced power consumption. In existing computing architectures, photonic data storage would dramatically improve the performance by reducing latencies associated with electrical memories. At the same time, the rise of ‘big data’ and ‘deep learning’ is driving the quest for non-von Neumann and brain-inspired computing paradigms. To succeed in both aspects, we have demonstrated non-volatile multi-level photonic memory avoiding the von Neumann bottleneck in the existing computing paradigm and a photonic synapse resembling the biological synapses for brain-inspired computing using phase-change materials (Ge₂Sb₂Te₅).

Citation Download Citation

Zengguang Cheng, Carlos Ríos, Nathan Youngblood, C. David Wright, Wolfram H. P. Pernice, and Harish Bhaskaran "On-chip phase-change photonic memory and computing", Proc. SPIE 10345, Active Photonic Platforms IX, 1034519 (24 August 2017); https://doi.org/10.1117/12.2272127

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