Defect Detection Using Regression Analysis of Transient Signal Data

Transient Signal Analysis is a digital device testing method that is based on the analysis of voltage transients at multiple test points and on IDD switching transients on the supply rails. We show that it is possible to identify defective devices by analyzing the transient signals measured at test points on paths not sensitized from the defect site. The small signal variations generated at these test points are analyzed in both the time and frequency domain. Linear regression analysis is used to show the presence of correlation in these signals across the outputs of defect-free devices. However, there is a definite lack of correlation between output signals of defective devices with bridging and open drain defects. We present an automatic procedure based on the mean and standard deviation of regression residuals that is able to distinguish between defect-free and defective devices. 1.0 Introduction Transient Signal Analysis (TSA) [1] is a parametric approach to testing digital integrated circuits. Defect detection is accomplished in TSA by analyzing the transient signals of a device measured simultaneously at multiple test points. The approach offers two distinct advantages over other logic and parametric testing methods. First, device coupling mechanisms permit the detection of defects at test points that are not on logic signal propagation paths from the defect site (off-path nodes) [2][3]. Consequently, direct observation of logic faults is not necessary in TSA. Second, the cross-correlation of multiple test point signals allows signal variations caused by process tolerances to be distinguished from those caused by defects. This is true because process tolerance effects tend to be global, causing signal changes on all test points of the device. In contrast, signal variations caused by a defect tend to be regional and more pronounced on test points closest to the defect site [4]. In this paper, we focus on the development of a statistical method that can be used to automate the TSA testing process. We introduce Signature Waveforms (SWs) as a means of capturing signal variations between defect-free and defective devices and formulate two statistics based on a compact representation