Advanced data analysis algorithms for the time-dependent defect spectroscopy of NBTI

In order to identify the physical mechanisms behind the negative bias temperature instability (NBTI), the time-dependent defect spectroscopy (TDDS) has been recently proposed. The TDDS takes advantage of the fact that in nano-scaled devices only a handful of defects are present. As a consequence, degradation and recovery proceed in discrete steps, each of them corresponding to a charge capture or emission event. By repeatedly applying stress and recovery conditions, the TDDS analyzes the statistical properties of these discrete events. The measurement window of the TDDS is very large, but the occurrence of random telegraph noise (RTN) at certain biases/temperatures can limit its applicability. We have developed an advanced data analysis method which can also deal with data contaminated by RTN. The algorithm is based on the combination of a bootstrapping technique and cumulative sum charts. A benefit of the new method is the possibility to detect steps in a large class of different signals with a feasible amount of parameters. Moreover, de-/trapping parameters of the random telegraph noise (RTN) become accessible as well.