NASA GSFC’s Thermal Signature Identification System (TheSIS) 1) measures the high order dynamic responses of
optoelectronic components to direct sequence spread-spectrum temperature cycling, 2) estimates the parameters of
multiple autoregressive moving average (ARMA) or other models the of the responses, 3) and selects the most
appropriate model using the Akaike Information Criterion (AIC). Using the AIC-tested model and parameter vectors
from TheSIS, one can 1) select high-performing components on a multivariate basis, i.e., with multivariate Figures of
Merit (FOMs), 2) detect subtle reversible shifts in performance, and 3) investigate irreversible changes in component or
subsystem performance, e.g. aging. We show examples of the TheSIS methodology for passive and active components
and systems, e.g. fiber Bragg gratings (FBGs) and DFB lasers with coupled temperature control loops, respectively.