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相关实验视频

Updated: Jul 9, 2025

Fabrication of Micropatterned Hydrogels for Neural Culture Systems using Dynamic Mask Projection Photolithography

Fabrication of Micropatterned Hydrogels for Neural Culture Systems using Dynamic Mask Projection Photolithography

Published on: February 11, 2011

逆光刻法物理知情深度神经水平设置用于面具优化.

Xing-Yu Ma, Shaogang Hao

|December 1, 2023

概括

我们介绍了一个反光刻法物理知情深度神经水平集 (ILDLS) 方法用于面具优化. 这种方法显著提高了集成电路的打印能力和工艺窗口,同时提高了与传统方法相比的计算效率.

科学领域:

集成电路制造集成电路制造
计算式 lithography 的使用方法.
深度学习应用程序

背景情况:

光学近距离校正 (OPC) 对高分辨率集成电路至关重要.
基于水平集的反光刻技术 (ILT) 提供了卓越的图案保真性,但却面临着高计算成本的困难.
深度学习 (DL) 可以加速ILT,但缺乏用于过程窗口增强的特定领域知识.

研究的目的:

开发一个高效的面具优化技术,用于集成电路光刻.
在先进的半导体制造中,提高可打印性和工艺窗口 (PW).
结合ILT和DL的优势,改进面具设计.

主要方法:

提出了一个反向光刻物理知情深度神经水平集 (ILDLS) 方法.
基于集成水平集的ILT作为深度学习框架中的一个层.
采用代口罩预测和纠正以进行优化.

主要成果:

与纯DL和ILT方法相比,ILDLS显著提高了可打印性和工艺窗口.
与传统的ILT相比,计算效率取得了实质性的改进.
证明了将基于物理的知识集成到DL中以优化面具的有效性.

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Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

Published on: February 12, 2017

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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Last Updated: Jul 9, 2025

Fabrication of Micropatterned Hydrogels for Neural Culture Systems using Dynamic Mask Projection Photolithography

Fabrication of Micropatterned Hydrogels for Neural Culture Systems using Dynamic Mask Projection Photolithography

Published on: February 11, 2011

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

Published on: February 12, 2017

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

结论:

ILDLS方法为半导体光刻学中面罩优化提供了一种新且高效的解决方案.
结合DL与反光刻法物理知识,克服了独立方法的局限性.
这种混合方法对先进的集成电路制造有希望.