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相关概念视频

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

264
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
264

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相关实验视频

Updated: Jul 9, 2025

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
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基于模型驱动的图形卷积网络的快速反向光刻方法.

Shengen Zhang, Xu Ma, Junbi Zhang

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    概括
    此摘要是机器生成的。

    这项研究引入了一种新型模型驱动的图形卷积网络 (MGCN),以加速集成电路制造的逆光刻技术 (ILT). 该MGCN框架显著提高了先进光学 lithography 的计算效率和图像准确性.

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    科学领域:

    • 半导体制造业 半导体制造业
    • 计算式 lithography 的使用方法.
    • 在工程领域的机器学习.

    背景情况:

    • 先进的光学光刻系统依赖逆光刻技术 (ILT) 来优化光罩传输功能,以提高图像保真度.
    • 传统的ILT面临着计算强度的挑战,这阻碍了它们在大量集成电路制造中得到广泛采用.

    研究的目的:

    • 开发一个高效的计算和高保真ILT框架,用于先进的光学光刻画.
    • 解决传统ILT在集成电路制造速度和资源要求方面的局限性.

    主要方法:

    • 提出了一个基于模型的图形卷积网络 (MGCN) 框架,集成密集的同心圆形采样 (DCCS) 来进行特征提取.
    • 一个基于GCN的编码器预测了优化的面具模式,其次是模拟光刻成像过程的模型驱动解码器.
    • 采用无监督培训策略,消除了需要耗时标记数据的需求.

    主要成果:

    • 通过利用DCCS和无监督方法,MGCN框架显著提高了ILT的计算效率.
    • 拟议的方法实现了高保真度成像结果,与最先进的ILT方法相比或超过.
    • 通过GPU框架上的并行计算,可以实现快速的面具预测.

    结论:

    • 在集成电路的大批量制造中,MGCN方法为加速ILT提供了可行的解决方案.
    • 与现有的ILT方法相比,该框架在计算速度和图像保真性方面都表现出卓越的性能.
    • 机器学习与 lithography 过程建模的整合为未来的进步提供了一个有希望的方向.