High-performance optoelectronics for integrated photonic neural networks

Martin Thomaschewski; Zibo Hu; Behrouz Movahhed Nouri; Yaliang Gui; Hao Wang; Salem Altaleb; Hamed Dalir; Volker J. Sorger

doi:10.1117/12.2650319

15 March 2023 High-performance optoelectronics for integrated photonic neural networks

Martin Thomaschewski, Zibo Hu, Behrouz Movahhed Nouri, Yaliang Gui, Hao Wang, Salem Altaleb, Hamed Dalir, Volker J. Sorger

Author Affiliations +

Proceedings Volume 12438, AI and Optical Data Sciences IV; 124380W (2023) https://doi.org/10.1117/12.2650319
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

Integrated optoelectronic devices represents a fundamental building block of hardware accelerators for photonics neural networks. Nanophotonic electro-optic modulators and detectors have significant performance advantages in power efficiency, communication bandwidth, and parallelism compared to conventional free-space photonics. Here, we present strategies and experimental validations of novel high-performance nanophotonic opto-electronic devices, involving heterogeneous integration of emerging materials into silicon photonic integrated circuits to exploit new functionality and device-scaling laws for efficient and ultrafast modulators, detectors, and photonic nonvolatile memory. The optoelectronic implementations of neural networks are demonstrated which significantly extends the spectrum of information processing capabilities.

Conference Presentation

Citation Download Citation

Martin Thomaschewski, Zibo Hu, Behrouz Movahhed Nouri, Yaliang Gui, Hao Wang, Salem Altaleb, Hamed Dalir, and Volker J. Sorger "High-performance optoelectronics for integrated photonic neural networks", Proc. SPIE 12438, AI and Optical Data Sciences IV, 124380W (15 March 2023); https://doi.org/10.1117/12.2650319

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