High-Speed Multifunctional Photonic Memory on a Foundry-Processed Photonic Platform
Sadra Rahimi Kari, Marcus Tamura, Zhimu Guo, Yi-Siou Huang, Hongyi, Sun, Chuanyu Lian, Nicholas Nobile, John Erickson, Maryam Moridsadat, Carlos, A. R\'ios Ocampo, Bhavin J Shastri, Nathan Youngblood

TL;DR
This paper introduces a foundry-compatible photonic memory cell that combines nonvolatile phase-change material-based weights with high-speed volatile tuning, enabling rapid, efficient, and multifunctional on-chip photonic memory for AI applications.
Contribution
A novel photonic memory device integrating phase-change materials with an integrated PN junction for dual nonvolatile and high-speed volatile tuning, fabricated via a foundry-compatible process.
Findings
Achieved 400-fold increase in volatile tuning speed.
Realized 10,000-fold improvement in tuning efficiency.
Demonstrated dual functionality of the same PN modulator.
Abstract
The integration of computing with memory is essential for distributed, massively parallel, and adaptive architectures such as neural networks in artificial intelligence (AI). Accelerating AI can be achieved through photonic computing, but it requires nonvolatile photonic memory capable of rapid updates during on-chip training sessions or when new information becomes available during deployment. Phase-change materials (PCMs) are promising for providing compact, nonvolatile optical weighting; however, they face limitations in terms of bit precision, programming speed, and cycling endurance. Here, we propose a novel photonic memory cell that merges nonvolatile photonic weighting using PCMs with high-speed, volatile tuning enabled by an integrated PN junction. Our experiments demonstrate that the same PN modulator, fabricated via a foundry compatible process, can achieve dual functionality.…
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Taxonomy
TopicsPhotonic Crystals and Applications · Photonic and Optical Devices · Neural Networks and Reservoir Computing
