Active Learning‐Driven Inkless Additive Nanomanufacturing for Printed Electronics

ABSTRACT Inkless additive nanomanufacturing for printed electronics promises broad material and substrate versatility, yet the high‐dimensional print parameter space makes tuning print parameters time‐intensive. We present a Bayesian optimization study that constructs a digital twin from printed‐silver data to benchmark surrogate models, acquisition functions, and batch sizes head‐to‐head to achieve user‐specified target resistance. Tested surrogate models included Gaussian process, random forest, and Bayesian neural network surrogates with expected improvement and confidence bound acquisition functions. In total, we evaluate 48 unique model configurations alongside a random sampling baseline for comparison. For printed silver, the Bayesian neural network with a batch size of one achieved the lowest average cumulative regret, approximately four times more efficient on average than random sampling. To balance performance and substrate space, a random forest model with expected improvement and a batch size of four was chosen as the model for validation testing. Applying this chosen configuration to copper with an additional print parameter, the model achieved a resistance within 0.15 Ω of a 1 Ω target in fewer than 30 printed lines across five validation sets. Overall, the workflow yields a tuned and validated model that efficiently guides experiments toward the target while simultaneously learning the parameter space.