Automated, high throughput optimization of multistep and cyclic etch and deposition processes using SandBox Studio AI
Automated, high throughput optimization of multistep and cyclic etch and deposition processes using SandBox Studio AI
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使用 SandBox Studio AI 对多步骤和循环蚀刻和沉积过程进行自动化、高通量优化
DOI：
10.1117/12.2614284
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Chopra, Meghali C.
中科院分区：
文献类型：
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作者：
Kearney, Kara;Medina, Leandro;Bonnecaze, Roger;Chopra, Meghali C.
由于工艺设计策略涉及具有不同产量和性能的依赖单元工艺，新技术和先进节点的开发是资本密集型的。这导致了对基于模型的优化的探索，以减少配方创建的成本和时间；然而，由于参数空间的多维性和实验数据的有限性，半导体工艺的计算优化具有很大的挑战性。SandBox Studio™AI是一种计算工具，可自动构建基于物理和机器学习的混合模型，该模型可用于预测最佳工艺配方，并探索新的工艺变化，如不同的传入掩模几何形状和步骤持续时间。在这里，我们展示了利用SandBox Studio™AI来构建高纵横比通道蚀刻的循环蚀刻和沉积过程的计算表示，该过程具有以下有害影响-弯曲，抵抗过度蚀刻，通过沉积堵塞和扭曲。该模型被校准为13个实验的合成数据集，具有5个不同的工艺参数。然后，预测出一个最优配方，使观察到的有害影响最小化。然后使用该模型来探索不同的传入掩模几何形状和步长，以进一步改进配方。这种能力是通过软件的基础物理模型实现的，而传统的统计和机器学习工具是不可能实现的。
The development of new technologies and advanced nodes is capitally intensive due to process design strategies that involve dependent unit processes with different yields and performances. This has led to the exploration of model-based optimization to cut the cost and time of recipe creation; however, computational optimization of semiconductor processes is quite challenging due to multi-dimensional parameter spaces and limited experimental data. SandBox Studio™ AI is a computational tool that automatically builds a hybrid physics-based and machine learning model that can be used to predict optimal process recipes and explore novel process changes such as different incoming mask geometries and step durations. Herein, we show the utilization of SandBox Studio™ AI to build a computational representation of a cyclic etch and deposition process of a high aspect ratio channel etch with the following detrimental effects – bowing, resist over-etching, clogging via deposition, and twisting. The model was calibrated to a synthetic data set of thirteen experiments with five varying process parameters. Then, an optimal recipe was predicted that minimized the observed detrimental effects. The model was then used to explore different incoming mask geometries and step durations to improve the recipe even further. This capability is made possible by the software’s foundational physics-based model and is not possible using conventional statistics and machine learning based tools.
通过基于模型的自动化工艺优化快速创建蚀刻配方
DOI：
10.1117/12.2583868
发表时间：
2021
期刊：
Advanced Etch Technology and Process Integration for Nanopatterning X
影响因子：
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作者：
Ban, Yang;Kearney, Kara;Sundahl, Bryan;Medina, Leandro;Bonnecaze, Roger T.;Chopra, Meghali J.
通讯作者：
Chopra, Meghali J.
3D NAND 结构的最佳蚀刻配方预测
DOI：
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发表时间：
2020
期刊：
Advanced Lithography
影响因子：
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作者：
Leandro Medina;Bryan E. Sundahl;Meghali Chopra;R. Bonnecaze
通讯作者：
R. Bonnecaze
使用基于模型的实验设计加速优化多层沟槽蚀刻
DOI：
--
发表时间：
2020
期刊：
Advanced Lithography
影响因子：
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作者：
Kara Kearney;Sonali N. Chopra;Xilan Zhu;Yang H. Ban;R. Bonnecaze;Meghali Chopra
通讯作者：
Meghali Chopra