Event Details

MARS seminar series. Speaker: Gabriele Scrivanti (Genoa) Title: A self-supervised approach for quantitative lifetime estimation in fluorescence microscopy for semiconductor and photovoltaic materials

Abstract: We propose an unsupervised deep-learning framework for the analysis of optical microscope data in the context of photovoltaic materials characterization. The goal is to recover spatially distributed carrier lifetime maps from a series of time-resolved fluorescence imaging (TR-FLIM) measurements. Accurate recovery of these parameters is critical for predicting solar cell efficiency and identifying early signatures of material degradation. However, observations can be photon-count-limited and corrupted by a challenging combination of noise sources whose joint statistical modelling is computationally intractable.

Our approach builds on the Noise2Noise (N2N) statistical learning framework, which exploits the theoretical result that a model trained to map one noisy realization of a signal to another independent noisy realization of the same signal converges, in expectation, to the underlying clean signal without ever requiring access to ground-truth data. We incorporate a physics-driven log-linear decay model into the N2N loss function, reflecting the known exponential decay structure of the emitted photoluminescence and enabling the simultaneous estimation of spatially varying amplitude and lifetime maps. The reconstruction is performed in a zero-shot regime, meaning no pre-training on external datasets is required: weight optimization is carried out directly on the set of experimental noisy observations available for a single scene.

This setting is well suited to low signal-to-noise ratio regimes arising from short acquisition protocols, which minimize potential damage caused by prolonged exposure to the laser excitation source. The method is demonstrated to be effective for the monitoring of lifetime evolution under controlled degradation conditions, including thermal stress and humidity exposure.

Joint work with Luca Calatroni,Stefania Cacovich and Tommaso Raimondi.