Remembrance of transistors past: Compact model parameter extraction using bayesian inference and incomplete new measurements

In this paper, we propose a novel MOSFET parameter extraction method to enable early technology evaluation. The distinguishing feature of the proposed method is that it enables the extraction of an entire set of MOSFET model parameters using limited and incomplete IV measurements from on-chip monitor circuits. An important step in this method is the use of maximum-A-posteriori estimation where past measurements of transistors from various technologies are used to learn a prior distribution and its uncertainty ma- trix for the parameters of the target technology. The frame- work then utilizes Bayesian inference to facilitate extraction using a very small set of additional measurements. The pro- posed method is validated using various past technologies and post-silicon measurements for a commercial 28-nm pro- cess. The proposed extraction could also be used to charac- terize the statistical variations of MOSFETs with the signi-cant benet that some constraints required by the backward propagation of variance (BPV) method are relaxed.