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We have analyzed a limited sample set of 120 GHz, and 150 GHz time-resolvedmillimeter wave (mmW) photoconductive decay (mmPCD) signals of 300 nm thickair-stable encapsulated perovskite film (methyl-ammonium lead halide) excitedusing a pulsed 532-nm laser with fluence 10.6 micro-Joules per cm-2. Wecorrelated 12 parameters derived directly from acquired mmPCD kinetic-tracedata and its step-response, each with the sample-age based on the date of theexperiment. Five parameters with a high negative correlation with sample agewere finally selected as predictors in the Gaussian Process Regression (GPR)machine learning model for prediction of the age of the sample. The effects ofaging (between 0 and 40,000 hours after film production) are quantified mainlyin terms of a shift in peak voltage, the response ratio (conductanceparameter), loss-compensated transmission coefficient, and the radiofrequency(RF) area of the transient itself (flux). Changes in the other step-responseparameters and the decay length of the aging transients are also shown. The GPRmodel is found to work well for a forward prediction of the age of the sampleusing this method. It is noted that the Matern-5 over 2 GPR kernel forsupervised learning provides the best realistic solution for age predictionwith R squared around 0.97.

Supervised learning applied to high-dimensional millimeter wave transient absorption data for age prediction of perovskite thin-film