The ever-increasing complexity of integrated circuits inevitably leads to high test cost. Adaptive testing provides an effective solution for test-cost reduction; this testing framework selects the important test items for each set of chips. However, adaptive testing methods designed for digital circuits are coarse-grained, and they are targeted only at systematic defects. To incorporate fabrication variations and random defects in the testing framework, we propose a fine-grained adaptive testing method based on machine learning. We use the parametric test results from the previous stages of test to train a quality-prediction model for use in subsequent test stages. Next, we partition a given lot of chips into two groups based on their predicted quality. A test-selection method based on statistical learning is applied to the chips with high predicted quality. An ad hoc test-selection method is proposed and applied to the chips with low predicted quality. Experimental results using a large number of fabricated chips and the associated test data show that to achieve the same defect level as in prior work on adaptive testing, the fine-grained adaptive testing method reduces test cost by 90% for low-quality chips and up to 7% for all the chips in a lot.

中文翻译：

---

基于质量预测的细粒度自适应测试

集成电路不断增加的复杂性不可避免地导致高昂的测试成本。自适应测试为降低测试成本提供了有效的解决方案；该测试框架为每组芯片选择重要的测试项目。然而，为数字电路设计的自适应测试方法是粗粒度的，它们只针对系统缺陷。为了将制造变化和随机缺陷纳入测试框架，我们提出了一种基于机器学习的细粒度自适应测试方法。我们使用来自先前测试阶段的参数测试结果来训练质量预测模型，以用于后续测试阶段。接下来，我们根据预测的质量将给定数量的芯片分成两组。将基于统计学习的测试选择方法应用于具有高预测质量的芯片。提出了一种自组织测试选择方法，并将其应用于预测质量低的芯片。使用大量制造芯片和相关测试数据的实验结果表明，为了达到与先前自适应测试工作相同的缺陷水平，细粒度自适应测试方法将低质量芯片的测试成本降低了 90% 及以上很多筹码中的所有筹码为 7%。