Year: 2024
Type: article
Abstract: \nWe describe our use of deep learning to optimize the multi-dimensional parameter space of systems-on-chip as an important step towards the scalable production of photonic solutions and their widespread integration into high-volume applications. The challenges of transitioning between prototype and volume production are highlighted, and the suitability of deep neural networks for navigating the multi-dimensional design space of today’s photonic circuits is discussed. We adopt multi-path neural network architectures to reduce the computational requirements of model training and to mitigate the risk of overfitting. We demonstrate the use of a multi-path neural network to optimize the construction parameters of photonic designs in a high-volume production environment. Lastly, we discuss the advantages of using machine learning not only as a highly capable tool for navigating the multi-dimensional design space of complex systems-on-chip but also as an effective strategy for compensating for fabrication process non-uniformities that are undetectable by standard process metrology instruments.\n
Authors: Ksenia Yadav, Serge Bidnyk, Ashok Balakrishnan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6959911607
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