EPreNet: A Condition-Guided Network Accelerates Etching Profile Prediction

Plasma etching is a critical step in semiconductor manufacturing, yet existing approaches are either computationally expensive or limited to predicting scalar etching metrics rather than full profile evolution. We propose EPreNet, a condition-guided spatio-temporal network for pixel-level prediction of plasma etching profile evolution from historical profile frames and process parameters. To support this task, we construct a benchmark dataset of 18,360 images spanning 918 process conditions simulated via TCAD, sampled with Latin Hypercube Sampling to ensure uniform parameter-space coverage, and further establish an evaluation framework combining image-level and geometry-based metrics for etching-profile prediction. Experiments demonstrate that EPreNet reduces MSE by 16% and achieves SSIM of 0.992 and PSNR of 30.323 dB, while achieving manufacturing-relevant geometric accuracy with 1.4° sidewall angle error and 1.6% depth error rate. Inference requires only 38.92 ms per frame faster than TCAD simulation 1300 s, supporting rapid surrogate-based evaluation and accelerated TCAD-assisted process exploration. The model also shows strong generalization to unseen initial critical dimensions and encouraging initial transferability to preprocessed experimental SEM images, suggesting its potential as an efficient surrogate for TCAD-assisted process development while maintaining high geometric fidelity.