Machine learning enabled optimization of showerhead design for semiconductor deposition process
Machine learning enabled optimization of showerhead design for semiconductor deposition process
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DOI：
10.1007/s10845-023-02082-8
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发表时间：
2023-02-15
影响因子：
8.3
通讯作者：
Gu，Grace X.
中科院分区：
文献类型：
--
作者：
Jin，Zeqing;Lim，Dahyun Daniel;Gu，Grace X.
在半导体制造中，沉积过程产生多层材料来实现绝缘和导电功能。沉积薄膜厚度的均匀性是制造高性能半导体器件的关键。实验和数值研究都证明，调整制造工艺参数(例如，对于半导体晶片上均匀分布的气流)是影响薄膜均匀性的主要因素之一。用来改变和测试一系列制造条件的传统试验和错误方法是耗时的，并且很少有研究探索改变硬件组件的几何形状的影响，例如淋浴喷头。在这里，我们提出了一种基于数值模拟数据的基于机器学习代理模型的喷头设计优化方法。开发和实施了精确的机器学习模型和优化算法，与基准传统喷头设计相比，流量均匀度提高了10%。此外，与传统方法相比，改进的贝叶斯优化方法在获得最优喷头设计方面节省了10倍的计算成本。这个支持机器学习的优化平台显示了良好的结果，可以应用于各种制造系统中的其他优化问题，如半导体制造和添加剂制造。
In semiconductor fabrication, the deposition process generates layers of materials to realize insulating and conducting functionality. The uniformity of the deposited thin film layers’ thickness is crucial to create high-performance semiconductor devices. Tuning fabrication process parameters (e.g., for evenly distributed gas flow on the semiconductor wafer) is one of the dominant factors that affect film uniformity, as evidenced by both experimental and numerical studies. Conventional trial and error methods employed to change and test a range of fabrication conditions are time-consuming, and few studies have explored the effect of changing the geometry of hardware components, such as the showerhead. Here, we present a design optimization of the showerhead for flow uniformity based on numerical simulation data using machine learning surrogate models. Accurate machine learning models and optimization algorithms are developed and implemented to achieve 10% more flow uniformity compared to a benchmark traditional showerhead design. Moreover, the developed Bayesian optimization method saves 10-fold computational cost in reaching the optimal showerhead designs compared to conventional approaches. This machine learning enabled optimization platform shows promising results which could be implemented for other optimization problems in various manufacturing systems such as semiconductor fabrication and additive manufacturing.