With increasing process complexity, quantifying impact of process parameters on scaling performance is of utmost importance. Edge placement error (EPE) budget is a key limiter for scaling. We present a Machine learning based analytics framework to perform impact analysis of various process assumptions on EPE. This would help us identify in advance the process bottlenecks for emerging nodes. For imec N3 process, one key challenge is via to buried power rail overlap which is shown to have a dependence on 15 process parameters. We first generate an exhaustive dataset of overlap EPE errors with respect to process assumptions using Monte Carlo simulation. Then a neural network model was built to model EPE error for given process parameters. Our engine ranked imec N3 litho process priorities using sensitivity analysis and identified key process bottlenecks. An impact analysis was performed to demonstrate 18% improvement in overlap edge placement error. This methodology can be used to guide the community about direction of early technology path-finding to optimize device performance.

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Apoorva Oak, "Quantifying process parameter impact on edge placement error using machine learning based analytical engine," Proc. SPIE 12052, DTCO and Computational Patterning, 1205209 (Presented at SPIE Advanced Lithography + Patterning: April 26, 2022; Published: 13 June 2022); https://doi.org/10.1117/12.2615607.6305846366112.