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Process Condition Optimization for Atomic Layer Etching: Insights into Surface Phenomena from Multiscale Computational Framework and Experiments
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DOI：10.1016/j.vacuum.2025.114864.png)
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Abstract
En 中文
Atomic layer etching (ALE) is a manufacturing methodology enabled by self-limiting chemical modification steps. Correlating the macroscale processing conditions to the nanoscale manufacturing features, including the etching rate and surface roughness are critical to the quality of the product and the efficiency of the manufacturing process. Here, we propose a multi-scale computational framework based on finite element analyses and cellular method. The numerical analyses reproduce the plasma properties, implemented in the cellular method to capture profile evolution at the atomic level. The etching rate and surface roughness predicted numerically are highly consistent with the experimental observation, enabling layer-by-layer etching on the SiO2 substrate (3.5 Å per cycle) and a reduced surface roughness of 1.2 Å. A parametric study on multiple processing conditions is carried out, including the coil power, bias voltage, and etching time. Given the different etching outcomes under different processing conditions, the minimum etching time realizing the ALE is proposed to optimize the efficiency of ALE. By investigating the mechanism through which ALE affects surface roughness, we found that while ALE results in slight differences in planar roughness under varying process conditions, its impact varies significantly with the predefined roughness surface.
Keywords:
Atomic layer etching
Multiscale computational framework
Finite element method
Cellular method
Etch per cycle
Surface roughness
Journal
IF:
3.9
Papers:
1.4W
Citations:
2.5W