Creating new multistep etch and deposition processes with recycled etch data using SandBox Studio AI™
Creating new multistep etch and deposition processes with recycled etch data using SandBox Studio AI™
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使用 SandBox Studio AI™ 使用回收的蚀刻数据创建新的多步蚀刻和沉积工艺
DOI：
10.1117/12.2614301
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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.
确定多步骤和循环蚀刻工艺的最佳配方是一个主要挑战，其中每一步的结果取决于前一步的进展。选择每个步骤的顺序和持续时间通常是通过繁琐的试错过程来完成的，其中实验试验的数量随着过程的复杂性呈指数级增长。在这里，我们提出了一种基于模拟的方法，可以显著加快这一过程。我们使用在各种工艺条件下获得的有限实验数据，包括压力、气体类型、气体流速、功率、偏置和时间，来校准基于阶跃感知的降阶物理蚀刻和沉积模型。该模型用于生成具有任意所需顺序和持续时间的步骤的预测。校准模型预测的顺序，定时，和可能的循环每一步，以实现所需的蚀刻目标。该方法在多层堆叠上演示了三个可能的步骤，包括蚀刻和沉积。结果表明，所提出的方法所需的实验总数明显少于全因子实验设计等标准方法所需的实验总数。我们还演示了如何使用蚀刻数据和由此校准的模型来确定不同孔径和/或掩膜几何形状的最佳蚀刻配方，而无需进行进一步的实验。
Identification of optimal recipes for multi-step and cyclic etch processes where the outcome of each step depends on the progression of the previous steps is a major challenge. Selecting the order and duration of each step is typically performed by a tedious trial and error process where the number of experimental trials scales exponentially with process complexity. Here we present a simulation-based methodology that significantly accelerates the process. We use limited experimental data taken at various process conditions, which may include pressure, gas type, gas flow rate, power, bias, and time to calibrate a step-aware reduced-order physics-based etch and deposition model. This model is used to generate predictions with steps permuted in any desired order and duration. The calibrated model predicts ordering, timing, and possible cycling of each step to achieve desired etch targets. The methodology is demonstrated on a multilayer stack with three possible steps, including etch and deposition. It is shown that the total number of experiments required for the proposed methodology is significantly less than that required by standard methods like full-factorial design of experiment. We also demonstrate how the etch data and the resulting calibrated model can be used to determine the optimal etch recipe for different aperture and/or mask geometries without having to perform further experiments.