Method, device and equipment for estimating reaction etching selection ratio of laminated material

Through the machine learning dynamic potential function optimization mechanism of active learning iterative sampling and the adaptive time step algorithm, the accuracy and efficiency problems of the reactive etching selectivity ratio of stacked materials are solved, and the accurate prediction and process optimization of the etching behavior of complex stacked materials are achieved.

CN120808906APending Publication Date: 2025-10-17SEMICON TECH INNOVATION CENT(BEIJING) CORP +1
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Patent Information

Application Number
CN202510696957.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-27
Publication Date
2025-10-17

AI Technical Summary

Technical Problem

Existing technologies make it difficult to efficiently obtain the reactive etching selectivity of micro-scale stacked materials, especially in the multiphase interfaces and heterogeneous structures of complex stacked materials. Traditional methods find it difficult to accurately capture the material-specific kinetic behavior during the etching process, resulting in insufficient accuracy in estimating the etching selectivity.

Method used

An iterative optimization mechanism of machine learning dynamic potential function with active learning iterative sampling is adopted. By obtaining the basic data set of the stacked material, integrating and screening the initial samples, and combining the adaptive time step algorithm for molecular dynamics simulation, the reactive etching selectivity of the stacked material is estimated.

Benefits of technology

It achieves accurate prediction of the anisotropic etching behavior of complex stacked materials, significantly reduces computing resource consumption, improves the estimation accuracy and computational efficiency of etching selectivity, and supports shortening the atomic-level process simulation cycle at the semiconductor manufacturing end.

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Abstract

The invention discloses a method, a device and equipment for estimating a reaction etching selection ratio of a laminated material, relates to the technical field of etching in microelectronic manufacturing, and is used for solving the problem that a micro-scale etching selection ratio is difficult to efficiently obtain in the prior art. Comprising the steps of obtaining a basic data set of a laminated material; integrating and screening the basic data set to obtain an initial sample; based on the initial sample, optimizing the basic data set by adopting an iterative optimization mechanism of a machine learning dynamic situation function of active learning iterative sampling to obtain a final potential function; and performing molecular dynamics simulation based on the final potential function, and estimating the reaction etching selection ratio of the laminated material in combination with an adaptive time step algorithm. According to the invention, a high-precision machine learning potential function can be formed and a dynamic model of an etching process can be completed; and quantitatively calculating the reaction rate difference of each layer of material, and deducing the etching selection ratio.
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