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Updated: Jan 10, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

A Two-Stage Unet Framework for Sub-Resolution Assist Feature Prediction.

Mu Lin¹, Le Ma^2,3, Lisong Dong^2,3

¹Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.

|November 27, 2025

Summary

This summary is machine-generated.

This study introduces a novel two-stage Unet framework for predicting sub-resolution assist feature (SRAF) parameters, improving lithography accuracy. The method enhances image fidelity by significantly reducing pattern and edge placement errors.

Keywords:

adaptive hybrid attention mechanism sub-resolution assist feature two-stage Unet warm-up cosine annealing algorithm

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Area of Science:

Semiconductor manufacturing
Photolithography
Computational imaging

Background:

Sub-resolution assist features (SRAFs) are crucial for enhancing contrast and process windows in advanced lithography.
Existing SRAF methods (model-based, rule-based, and end-to-end learning) face limitations in adaptability, computational cost, or precise geometric parameter extraction.

Purpose of the Study:

To develop an effective learning-based method for precise SRAF parameter prediction, specifically for Manhattan SRAFs.
To improve the accuracy and efficiency of SRAF pattern generation in lithography.

Main Methods:

A two-stage Unet framework is proposed for predicting SRAF polygon centroid coordinates and dimensions.
An adaptive hybrid attention mechanism is integrated to enhance feature integration and prediction accuracy.
A warm-up cosine annealing learning rate strategy is employed for stable and faster training.

Main Results:

The proposed method accurately and rapidly estimates SRAF parameters.
Significant reductions in mean pattern error (PE) from 25,776.44 to 15,203.33 and edge placement error (EPE) from 5.8367 to 3.5283 were achieved.
The method demonstrates superior performance in predicting SRAF patterns compared to traditional neural networks.

Conclusions:

The two-stage Unet framework with an adaptive hybrid attention mechanism offers an effective solution for SRAF parameter prediction.
This approach significantly enhances image fidelity in lithography systems.
The method overcomes limitations of previous SRAF techniques, offering improved accuracy and efficiency.