  Transfer learning increasingly becomes an important tool in handling data
scarcity often encountered in machine learning. In the application of
high-throughput thickness as a downstream process of the high-throughput
optimization of optoelectronic thin films with autonomous workflows, data
scarcity occurs especially for new materials. To achieve high-throughput
thickness characterization, we propose a machine learning model called
thicknessML that predicts thickness from UV-Vis spectrophotometry input and an
overarching transfer learning workflow. We demonstrate the transfer learning
workflow from generic source domain of generic band-gapped materials to
specific target domain of perovskite materials, where the target domain data
only come from limited number (18) of refractive indices from literature. The
target domain can be easily extended to other material classes with a few
literature data. Defining thickness prediction accuracy to be within-10%
deviation, thicknessML achieves 92.2% (with a deviation of 3.6%) accuracy with
transfer learning compared to 81.8% (with a deviation of 3.6%) 11.7% without
(lower mean and larger standard deviation). Experimental validation on six
deposited perovskite films also corroborates the efficacy of the proposed
workflow by yielding a 10.5% mean absolute percentage error (MAPE).
