Machine learning-driven soft plasma etching for precision structuring of biofunctional organic semiconductor films

Abstract

This study pioneers a novel in situ strategy for depth-resolved analysis of organic thin films by integrating soft plasma etching with real-time spectroscopy. This method achieves molecular-level selectivity by combining Bayesian-optimized CNN-LSTM neural networks with spectral reconstruction algorithms, and we establish a closed-loop optimization framework that predicts etching parameters with sub-nanometer accuracy, thereby enhancing carrier mobility by 42% and reducing interface trap density to below 10¹¹ cm² eV. Depth-resolved spectroscopic profiling successfully reveals vertical composition gradients in blend films and quantifies spatial charge regulation mechanisms in transistors. This approach provides a universal platform for optimizing exciton distribution in organic photovoltaics, enhancing charge transport in flexible electronics, and advancing molecular-level design of high-performance organic semiconductor devices.