Advanced Chemical-mechanical polishing (CMP) process not only needs to maintain stable run-to-run thickness control but also achieve better within wafer/within chip planarization performance. Furthermore, slurries or other consumable parts, like PAD and Disks selection are also the keys for CMP process optimization. The most difficult thing in CMP process is to have capability to predict and cover the various topologies and layout densities patterned wafers and preventing the hot spots occurrences. In this study, different Neural-Network algorithm with data pre-processing models are implemented to the in-line CMP CLC tuning and dishing/erosion prediction at various topology/pattern density test vehicle pattern wafers. Transfer learning technique is implemented on the original Neural -Network algorithm model, the behavior of real product can be simulated and predicted based on the knowledge of test vehicle database successfully. With the aid of multiple layer erosion/ dishing Neural-Network algorithm model prediction, the potential high risky hot spots can be highlighted at the initial layout design stage, then further shorten the turn-around time of design layout validation.

中文翻译：

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通过机器学习进行 CMP 工艺优化工程


先进的化学机械抛光（CMP）工艺不仅需要保持稳定的运行厚度控制，而且还需要实现更好的晶圆内/芯片内平坦化性能。此外，浆料或其他易损件，如PAD和Disks的选择也是CMP工艺优化的关键。 CMP工艺中最困难的事情是能够预测和覆盖图案化晶圆的各种拓扑和布局密度并防止热点的出现。在本研究中，采用具有数据预处理模型的不同神经网络算法来对各种拓扑/图案密度测试车辆图案晶圆进行在线 CMP CLC 调整和凹陷/侵蚀预测。迁移学习技术是在原有的神经网络算法模型上实现的，可以根据测试车辆数据库的知识成功地模拟和预测实际产品的行为。借助多层侵蚀/凹陷神经网络算法模型预测，可以在布局设计初始阶段突出潜在的高风险热点，从而进一步缩短设计布局验证的周转时间。