Accurate Edge Feature Prediction and Alignment in Mask-Aligned Lithography via Adversarial Hyper-Resolution Networks

**Abstract:** Mask-aligned lithography (MAL) faces increasingly stringent requirements for edge placement accuracy as feature sizes shrink to sub-10nm. Conventional methods rely heavily on empirical models and process control, often lacking the ability to predict and compensate for complex edge distortions and variations. This paper proposes an innovative framework, Adversarial Hyper-Resolution Networks (AHRN), combining generative adversarial networks (GANs) and multi-scale feature extraction to accurately predict and align edge features in MAL systems. AHRN is fundamentally new as it dynamically learns and compensates for lithographic distortions, enabling unprecedented edge placement accuracy beyond the limitations of current empirical models. We project substantial improvements in chip yield (5-10%), reduced process variation (geometric mean reduction of 20-30%), and accelerated process development cycles (2x reduction in experimentation time), with potential for extension to other advanced patterning techniques beyond MAL. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*