Machine learning for design optimizations and prediction of optical chip performance

Abstract

Artificial intelligence (AI) and machine learning (ML) have tremendous potential for increasing the scale and reach of the photonics industry. We present how the use of AI/ML has revolutionized the field of photonic integrated circuit design and manufacturing, and resulted in mass deployments of high-performance optical chips for multiple classes of datacom and telecom applications. First, we discuss our use of a deep neural network multivariate regression model to optimize the individual design parameters of hundreds of optical chips on a given mask. This work successfully addresses the systematic processing variations within a wafer, resulting in an unprecedented homogeneity of performance of optical chips in a high-volume production environment. Second, we present our approach of using ML to predict the performance of optical devices by wafer probing. This novel approach eliminates the expensive and time-consuming process of optical chip testing and instead relies on a wafer probe measurement to infer the performance of hundreds of chips on a wafer. We discuss the complexity of the problem of predicting the performance in multi-dimensional parameter space, the inherent challenges that cannot be overcome by traditional methods, and the reasons why ML is an essential tool to solve this problem. The support vector machine (SVM) that we developed performs nonlinear binary classification based on a regression from the probe measurement, allowing unprecedented control over our process, including in-situ monitoring of wafer fabrication and real-time process adjustments, and thus achieving consistently high performance of optical chips at high production volumes.