Predicting and optimizing etch recipes for across the wafer uniformity
Predicting and optimizing etch recipes for across the wafer uniformity
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预测和优化蚀刻配方以实现整个晶圆的均匀性
DOI：
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发表时间：
2019
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通讯作者：
Sofia Helpert
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作者：
Meghali Chopra;R. Bonnecaze;Yang H. Ban;Sofia Helpert
随着临界尺寸（CD）和容差的缩小，整个晶片上的临界尺寸（CD）的均匀性是越来越大的挑战。等离子体蚀刻均匀性部分地通过反应器设计并且部分地通过反应器的操作条件或工艺配方来实现。识别用于特定蚀刻工艺的配方是耗时且昂贵的，需要大量的实验和计量。在这里，我们介绍了SandBox StudioTM中的两个模块，SB-Bayesian和SBNeuralNet，以加速整个晶圆均匀性的蚀刻配方的预测和优化。创建跨晶片的蚀刻速率的模型，该模型考虑注射器位置、气体流速和分布以及等离子体功率。在300 mm晶片上进行了蚀刻线间距图案的合成实验，并计算了数百个位置处的CD及其变化。SB-Bayesian需要更少的实验进行校准，并实现与实验数据的良好定性匹配。SB-NeuralNet在预测平均CD和均匀性方面达到了与SBBayesian相当的准确度，但在预测整个晶圆的趋势方面表现不佳。结果表明，神经网络需要一个禁止量的实验数据，成功地预测晶片图案。SBBayesian和SB-NeuralNet用于在感兴趣的参数空间中创建详细的过程图，以确定最佳配方，以实现所需的CD和公差。这两个模块都可以预测实现确定的目标CD和均匀性指标的最佳配方条件。使用这些工具，以较低的成本快速优化整个晶片均匀性的蚀刻配方。
Uniformity of critical dimensions (CDs) across a wafer is an increasing challenge as both CDs and tolerances shrink. Plasma etch uniformity is achieved in part through reactor design and in part through the operating conditions or process recipe of the reactor. The identification of a recipe for a specific etch process is time consuming and expensive, requiring extensive experiments and metrology. Here we present two modules in SandBox StudioTM, SB-Bayesian and SBNeuralNet, to accelerate the prediction and optimization of etch recipes for across the wafer uniformity. A model of etch rates across the wafer is created that accounts for injector locations, gas flow rates and distribution and plasma powers. Synthetic experiments on etching line-space patterns on 300 mm wafers are performed and the CDs and their variations are computed at several hundred site locations. SB-Bayesian requires many fewer experiments to be calibrated and achieve an excellent qualitative match with the experimental data. SB-NeuralNet achieves comparable levels of accuracy to SBBayesian at predicting average CDs and uniformity, but it does not perform as well at predicting trends across the wafer. It is shown that neural nets require a prohibitive amount of experimental data to successfully predict wafer patterns. SBBayesian and SB-NeuralNet were used to create detailed process maps across the parameters space of interest to identify optimal recipes to achieve required CDs and tolerances. Both modules can predict optimal recipe conditions for achieving identified target CD and uniformity metrics. Using these tools, etch recipes for across the wafer uniformity are rapidly optimized at lower cost.