Markov-Chain Formulation of Reaction-Diffusion Model and its Implications for Statistical Distribution of Interface Defects in Nanoscale Transistors

Continued scaling of nanoscale transistors leads to broad device-to-device fluctuation of parameters due to random dopant effects, channel length variation, interface trap generation, etc. In this paper, we obtain the statistics of negative bias temperature instability (NBTI)-induced interface defect generation in ultra-scaled MOSFET by Markov Chain Monte-Carlo (MCMC) solution of Reaction-Diffusion (R-D) model. Our results show that the interface defect generation at a particular stress time, i.e., NIT}@tSTS in small transistors should follow a skew-normal distribution and that the generation and annealing of interface defects are strongly correlated. Next, we use a random percolative network to demonstrate (which is also consistent with previously published results in literature based on separate techniques) that the distribution of threshold voltage shift for single interface defect, i.e., ΔVT@NIT is exponential, with finite number of transistors having zero ΔVT. Finally, we show that the statistics of ΔVT@tSTS - based on the convolution of NIT@tSTS and ΔVT@NIT - is broadly consistent with the available experimental data in literature.

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