机器学习在评估光子集成电路QoT损伤以降低系统裕度中的有效性

2020 IEEE Photonics Conference (IPC) Pub Date : 2020-09-01 DOI:10.1109/IPC47351.2020.9252247

I. Khan, M. Chalony, E. Ghillino, M. U. Masood, J. Patel, D. Richards, P. Mena, P. Bardella, A. Carena, V. Curri

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引用次数: 8

摘要

我们提出了一种评估集成电路QoT损伤的机器学习技术。研究了开关元件的裕度缩减问题。机器学习训练的总体结果和数据集是通过利用Synopsys光子设计套件的集成软件环境获得的。

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Effectiveness of Machine Learning in Assessing QoT Impairments of Photonics Integrated Circuits to Reduce System Margin

We propose machine learning technique for assessment of QoT impairments of integrated circuits. We consider margin reduction problem applied to a switching component. Overall results and data sets for machine-learning training are obtained by leveraging the integrated software environment of the Synopsys Photonic Design Suite.

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2020 IEEE Photonics Conference (IPC)

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