论文标题
深度学习辅助管道，用于有效的后硅质调整
A Deep-Learning-Aided Pipeline for Efficient Post-Silicon Tuning
论文作者
论文摘要
在后硅验证中，调整是为了找到调谐旋钮的值，这可能是过程参数和/或已知操作条件的函数。从这个意义上讲，更有效的调整需要根据测试的设备（DUT）来确定最关键的调整旋钮和过程参数。这通常是由经验丰富的专家手动进行的。但是，随着越来越复杂的芯片，对大量原始变量的手动检查变得更具挑战性。在这项工作中，我们利用神经网络有效地选择最相关的变量，并提出相应的深度学习辅助管道进行有效的调整。
In post-silicon validation, tuning is to find the values for the tuning knobs, potentially as a function of process parameters and/or known operating conditions. In this sense, an more efficient tuning requires identifying the most critical tuning knobs and process parameters in terms of a given figure-of-merit for a Device Under Test (DUT). This is often manually conducted by experienced experts. However, with increasingly complex chips, manual inspection on a large amount of raw variables has become more challenging. In this work, we leverage neural networks to efficiently select the most relevant variables and present a corresponding deep-learning-aided pipeline for efficient tuning.
