Deep Learning-Based Prediction of Tool Influence Function for Nanometric Control in Space Optical Material

Abstract

Polishing is a critical process in fabricating space-telescope mirrors because it determines the surface figure and consequently optical performance. Deterministic polishing relies on the tool influence function (TIF), which describes the spatial materialremoval profile. At nanometric removal depths, the TIF becomes highly sensitive to process conditions, limiting the accuracy of analytic models such as Preston’s equation. In this study, we propose a deep learning-based approach to predict TIF depth for polishing a Silicon Carbide (SiC) mirror surface. To mitigate data scarcity, we augment 231 experimental measurements with Gaussian noise consistent with the repeatability observed in repeated trials (- 20 nm peak-to-peak). The resulting model achieves a validation mean absolute error (MAE) of 4.24 nm and a test MAE of 3.99 nm; on nine additional experimental cases, the MAE is 6.75 nm. These results indicate that the proposed augmentation improves robustness to experimental variability and supports the development of a data-driven, automated polishing workflow