Associative learning on phase change photonics

James, Y. S. Tan; Zengguang Cheng; Johannes Feldmann; Xuan Li; Nathan Youngblood; Utku E. Ali; C. David Wright; Wolfram H. P. Pernice; Harish Bhaskaran

doi:10.1117/12.2593248

2 August 2021 Associative learning on phase change photonics

James, Y. S. Tan, Zengguang Cheng, Johannes Feldmann, Xuan Li, Nathan Youngblood, Utku E. Ali, C. David Wright, Wolfram H. P. Pernice, Harish Bhaskaran

Author Affiliations +

Proceedings Volume 11796, Active Photonic Platforms XIII; 117961U (2021) https://doi.org/10.1117/12.2593248
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

Associative learning as a building block for machine learning network is a largely unexplored area. We present in this paper our results on the demonstration of an all optical associative learning element, realized on an integrated photonic platform using phase change materials combined with on-chip cascaded directional couplers. We implement the framework on our optical on-chip associative learning network, and experimentally demonstrate image classification on a publicly-accessible cat-dog dataset. The experimental implementation harnesses optical wavelength division-multiplexing, thus increasing the information channel capacity to process our machine learning task. Our unconventional approach to machine learning demonstrated experimentally on an optical platform could potentially open up new research possibilities in machine learning hardware architectures and algorithms.

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James, Y. S. Tan, Zengguang Cheng, Johannes Feldmann, Xuan Li, Nathan Youngblood, Utku E. Ali, C. David Wright, Wolfram H. P. Pernice, and Harish Bhaskaran "Associative learning on phase change photonics", Proc. SPIE 11796, Active Photonic Platforms XIII, 117961U (2 August 2021); https://doi.org/10.1117/12.2593248

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