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

被引：0

作者：

Khan, Ihtesham ^{[1
]}

Chalony, Maryvonne ^{[2
]}

Ghillino, Enrico ^{[3
]}

Masood, M. Umar ^{[1
]}

Patel, Jigesh ^{[3
]}

Richards, Dwight ^{[4
]}

Mena, Pablo ^{[3
]}

Bardella, Paolo ^{[1
]}

Carena, Andrea ^{[1
]}

Curri, Vittorio ^{[1
]}

机构：

[1] Politecn Torino DET, Turin, Italy

[2] Light Tec SARL, Hyeres, France

[3] Synopsys Inc, New York, NY USA

[4] CUNY Coll Staten Isl, New York, NY USA

来源：

2020 IEEE PHOTONICS CONFERENCE (IPC) | 2020年

关键词：

Machine learning; Photonic Integrated Circuits; Q-factor;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：2

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