Earticle

초록

영어

In semiconductor manufacturing, the design of devices and the selection of optimal materials are traditionally time-consuming and costly process. To address these challenges, machine learning techniques are being explored to improve simulation speed and efficiency without compromising accuracy. This study aims to optimize semiconductor manufacturing by identifying the optimal material based on slit design structure and transmittance. Traditionally, inverse design methods focused on developing slit designs from transmittance, requiring significant time and financial resources for material validation through simulations. We propose a multi-modal algorithm that combines slit design images and transmittance to predict optimal material. Additionally, we introduce a convolutional neural network that predicts transmittance from slit design image and materials. Our approach introduces a model that identifies optimal materials directly from transmittance and design structure, enhancing efficiency. This advancement allows for effective prediction and analysis of material properties in semiconductor devices through domain transformation.

목차

Abstract
I. INTRODUCTION
II. BACKGROUND
A. Multi-modal Machine Learning
B. CNN
III. MATERIAL AND METHODS
A. Dataset
B. Model Architecture
IV. EXPERIMENT
A. Training
B. Evaluation metrics
C. Results
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Seoyoung Sim [ School of Electrical Engineering Korea University Seoul, Korea ]
• Junhee Seok [ School of Electrical Engineering Korea University Seoul, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004