Plasma-surface interactions(PSI)play a crucial role in microelectronics fabrication;however,their multiscale nature and array of complex,often unknown interactions make computationalmodeling of PSIs extremely difficul...Plasma-surface interactions(PSI)play a crucial role in microelectronics fabrication;however,their multiscale nature and array of complex,often unknown interactions make computationalmodeling of PSIs extremely difficult.To this end,we propose a general neural master equation(NME)framework that uses master equations to describe the dynamics of a molecular process,wherein neural networks learned from atomistic simulations represent unknown transitions between different systemstates.By leveraging the physics-based structure of master equations and data-driven state transitions,the NME framework promotes generalizability and physics interpretability,and can bridge disparate length and time scales.The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching,where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters.The NME-based surface kinetic models obey physical constraints,which are violated in models based on neural ordinary differential equations.The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.展开更多
基金supported in part by the U.S.Department of Energy,Office of Science,Office of Fusion Energy Sciences under Award No.DE-SC0024472 and Award No.DE-SC0024474.
文摘Plasma-surface interactions(PSI)play a crucial role in microelectronics fabrication;however,their multiscale nature and array of complex,often unknown interactions make computationalmodeling of PSIs extremely difficult.To this end,we propose a general neural master equation(NME)framework that uses master equations to describe the dynamics of a molecular process,wherein neural networks learned from atomistic simulations represent unknown transitions between different systemstates.By leveraging the physics-based structure of master equations and data-driven state transitions,the NME framework promotes generalizability and physics interpretability,and can bridge disparate length and time scales.The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching,where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters.The NME-based surface kinetic models obey physical constraints,which are violated in models based on neural ordinary differential equations.The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.
