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Authors: Santiago Garcia-Cuevas Carrillo, A. Lugnan, Emanuele Gemo, P. Bienstman, Wolfram H. P. Pernice, Harish Bhaskaran, C. David Wright
Title: System-Level Simulation for Integrated Phase-Change Photonics
Format: International Journal
Publication date: 7/2021
Journal/Conference/Book: Journal of lightwave technology
Editor/Publisher: IEEE, 
Volume(Issue): 39(20) p. 6392 - 6402
DOI: 10.1109/JLT.2021.3099914
Citations: 8 ( - last update: 7/7/2024)
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Conventional computing systems are limited in performance by the well-known von Neumann bottleneck, arising from the physical separation of processor and memory units. The use of electrical signals in such systems also limits computing speeds and introduces significant energy losses. There is thus a pressing need for unconventional computing approaches, ones that can exploit the high bandwidths/speeds and low losses intrinsic to photonics. A promising platform for such a purpose is that offered by integrated phase-change photonics. Here, chalcogenide phase-change materials are incorporated into standard integrated photonics devices to deliver wide-ranging computational functionality, including non-volatile memory and fast, low-energy arithmetic and neuromorphic processing. We report the development of a compact behavioral model for integrated phase-change photonic devices, one which is fast enough to allow system level simulations to be run in a reasonable timescale with basic computing resources, while also being accurate enough to capture the key operating characteristics of real devices. Moreover, our model is readily incorporated with commercially available simulation software for photonic integrated circuits, thereby enabling the design, simulation and optimization of large-scale phase-change photonics systems. We demonstrate such capabilities by exploring the optimization and simulation of the operating characteristics of two important phase-change photonic systems recently reported, namely a spiking neural network system and a matrix-vector photonic crossbar array (photonic tensor core). Results show that use of our behavioral model can significantly facilitate the design and optimization at the system level, as well as expediting exploration of the capabilities of novel phase-change computing architectures.

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