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Leveraging the power of machine learning, we introduce a breakthrough approach in high-volume manufacturing of photonics chips for advanced applications. Despite the transformative potential of photonics in many industries, its widespread adoption has been hindered by multiple challenges in the fabrication of complex integrated chips. We deployed machine learning models with diverse architectures at every stage of our manufacturing process to overcome these challenges. Inevitable variations in the fabrication process often lead to performance variability among photonics chips on a single wafer and across different wafers. We effectively overcome this challenge by employing a deep neural network to study the variability in the performance of individual chips, enabling us to predict the precise optimizations necessary to compensate for inevitable process variations. We describe our selection of the deep neural network architecture that addresses this challenge, our methodology for obtaining a high-quality dataset for training, and the enhancements in performance uniformity achieved through machine learning-enhanced production masks. Moreover, our use of machine learning has allowed us to bypass the time-consuming and labour-intensive process of optical chip testing, which significantly limits the scalability of photonic deployments in high-volume applications. As a powerful alternative to such testing, we developed a new technology that relies on a wafer probe that collects metrology data from multitude of locations on an undiced wafer. Utilizing a support vector machine (SVM), we analyze this metrology data and employ nonlinear binary classification to accurately predict the performance of hundreds of chips on a wafer across various metrics. We describe the approach employed for data collection to train the model, the trade-offs involved in hyperparameter tuning, and our methodology for evaluating the predictive quality of the binary classifiers. Additionally, we highlight the new capability of in-situ monitoring of wafer fabrication, which enables high-volume production and deployment of photonic solutions.
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