Optimal etch recipe prediction for 3D NAND structures

We present a model-based experimental design methodology for accelerating 3D etch optimization with demonstration on 3D NAND structures. The design and optimization of etch recipes for such 3D structures face significant challenges requiring costly and time-consuming experiments in order to achieve the required tolerances. 3D NAND memory devices additionally require accurate nanofabrication of high aspect ratio trenches and isolation slits, which are challenging to manufacture reliably within specifications. Our model efficiently captures the relevant physical and chemical processes, which allows them to be calibrated using a limited number of experimental samples and can reproduce realistic 3D etch of multilayer materials, including bowing, necking, and tapering. Since our GPU-powered simulations run in a matter of minutes, the relevant process parameter space can be explored extensively in a short amount of time. The calibrated physics-based model can be used to train adaptive machine-learning-based heuristics which enable near-instant queries, for example for data visualization and analytics. With this approach, we show a rapid methodology for locating optimal windows in the process parameter space for etching 3D structures. Optimality metrics under consideration include both conformances to specified tolerances as well as robustness against process parameter variations. These techniques can reduce cost and time to market for complex multi-layer three-dimensional device designs and improve semiconductor device yields.