Smarter Semiconductors: How ML Neural Networks Optimize Plasma Etching in Real Time

This document is a working paper published for early dissemination and public access via Zenodo. It has not undergone formal peer review. The content reflects the author's original technical work, insights, and applied use of machine learning in real-world domains. This version is archived for academic and professional visibility and may be updated in the future based on community feedback or further development. The content was originally published as a technical article on prakashkota.com on May 1, 2025, and has been reformatted into this working paper for citation and archival purposes. Abstract This article presents a machine learning (ML) solution that transforms historical process data into accurate, real-time predictions of plasma etch rates. Utilizing a neural network trained on key operating conditions, the developed surrogate model consistently achieves sub-angstrom error, with over 97% of predictions falling within ±5 Å/min of actual values. The model enables predictive tuning without interrupting production, replacing costly experimentation with fast, data-driven insights. This approach offers semiconductor manufacturers a smarter, faster, and more agile method for process control, allowing them to convert legacy process data into strategic insights, accelerate development cycles, and unlock new efficiencies in plasma-based manufacturing.