Given the inherent perturbations during the fabrication process of integrated circuits that lead to yield loss, diagnosis of failing chips is a mitigating method employed during both yield ramping and high-volume manufacturing for yield learning. However, various uncertainties in the fabrication process bring a number of challenges, resulting in diagnosis with undesirable outcomes or low efficiency, including, for example, diagnosis failure, bad resolution, and extremely long runtime. It would therefore be very beneficial to have a comprehensive preview of diagnostic outcomes beforehand, which allows fail logs to be prioritized in a more reasonable way for smarter allocation of diagnosis resources. In this work, we propose a learning-based previewer, which is able to predict five aspects of diagnostic outcomes for a failing IC, including diagnosis success, defect count, failure type, resolution, and runtime magnitude. The previewer consists of three classification models and one regression model, where Random Forest classification and regression are used. Experiments on a 28 nm test chip and a high-volume 90 nm part demonstrate that the predictors can provide accurate prediction results, and in a virtual application scenario the overall previewer can bring up to 9× speed-up for the test chip and 6× for the high-volume part.

中文翻译：

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迈向更智能的诊断

鉴于集成电路制造过程中会导致良率损失的固有扰动，故障芯片的诊断是良率提升和大批量制造期间用于良率学习的一种缓解方法。然而，制造过程中的各种不确定性带来了许多挑战，导致诊断结果不理想或效率低下，例如诊断失败、分辨率差和运行时间极长。因此，事先全面预览诊断结果将是非常有益的，这允许以更合理的方式对故障日志进行优先级排序，从而更智能地分配诊断资源。在这项工作中，我们提出了一个基于学习的预览器，它能够预测故障 IC 诊断结果的五个方面，包括诊断成功、缺陷计数、故障类型、解决方案和运行时间量级。预览器由三个分类模型和一个回归模型组成，其中使用了随机森林分类和回归。在 28 nm 测试芯片和大批量 90 nm 零件上的实验表明，预测器可以提供准确的预测结果，在虚拟应用场景中，整体预览器可以为测试芯片带来高达 9 倍的加速和 6 倍的加速。对于大容量部分。