论文标题
基于高斯过程回归的功率MOSFET的自适应离群值检测
Adaptive Outlier Detection for Power MOSFETs Based on Gaussian Process Regression
论文作者
论文摘要
半导体设备的异常检测很重要，因为制造业的变化本质上是不可避免的。为了正确检测异常值，有必要考虑与潜在趋势的差异。常规方法不足，因为它们无法跟踪趋势的空间变化}}。这项研究提出了使用Gaussian工艺回归（GPR）和Student-T的可能性进行自适应离群值检测，该检测可能会逐渐捕获特征变化的空间变化。根据GPR后验分布的可靠间隔，检测到对基本趋势的偏差过多的设备。通过使用商业SIC晶片和仿真的实验来验证所提出的方法。
Outlier detection of semiconductor devices is important since manufacturing variation is inherently inevitable. In order to properly detect outliers, it is necessary to consider the discrepancy from underlying trend. Conventional methods are insufficient as they cannot track spatial changes of the trend}}. This study proposes an adaptive outlier detection using Gaussian process regression (GPR) with Student-t likelihood, which captures a gradual spatial change of characteristic variation. According to the credible interval of the GPR posterior distribution, the devices having excessively large deviations against the underlying trend are detected. The proposed methodology is validated by the experiments using a commercial SiC wafer and simulation.
