Scatterometry and machine learning for in-die overlay solution

Advanced technology nodes require tighter lithography overlay specifications with higher throughput and lower cost of ownership. Today, with the accelerating complexity of nanoelectronics for memory applications, an increased emphasis is placed on controlling the on-product overlay (OPO) budget. Consequently, accurate in-die overlay measurements play a critical role after the etching process (ACI) for which it can better reflect the actual product overlay. Here we propose a solution with the combined spectroscopic full Mueller matrix, measured with the KLA next-generation SpectraShape™ dimensional metrology system and a physics-based machine learning algorithm. Both real spectra collected by the SpectraShape and theoretical spectra generated from the scatterometry model are trained against their corresponding ground truth reference and synthetic reference data respectively to predict overlay. Theoretical and experimental results show that the Mueller elements are sensitive to very small changes in the overlay parameters which can enable inline, high-throughput overlay metrology. Accuracy, robustness, and precision on massive datasets using design of experiments (DOE) wafers are presented and discussed. Moreover, the measurement reliability is assessed with a key performance indicator (KPI), designed to flag a process excursion in a high-volume manufacturing (HVM) environment. Good agreement is observed between the KPI and the actual model accuracy.