An improved crack probability model for silicon oxide layers using three-parameter Weibull analysis

Abstract

For semiconductor dielectric layer crack detection in real-time we developed an efficient and very accurate method, which is based on acoustic emission (AE) testing and simulates the wafer probing process. The crack probability is determined using clustered and filtered AE data, that is collected by an advanced sensor-indenter system. The derived distribution function is based on two-parameter Weibull analysis, which is commonly applied for stress-induced failures of brittle materials like silicon oxide (SiO2). During indenter experiments, we observed, that the probability model for a higher number of data points is becoming inaccurate, especially at low fracture forces. Therefore, we introduce a more accurate statistical model for probing-induced SiO2 layer cracks, which is, according to a three-parameter Weibull distribution, considering a location parameter. Experimental results have shown, that the improved distribution function fits much better for predicting low-ppm failure rates of back-end-of-line (BEOL) stacks of CMOS technologies. This enables more reliable pad stack design rules and increases the reliability of complex chip designs regarding mechanical stress during test and assembly processes.