At the Farjam Research Group (FRG), we aim to understand, predict, and improve the behavior of complex dynamical systems — with a particular focus on advanced manufacturing processes such as nanomanufacturing, as well as emerging applications in robotics and autonomous systems. We view these systems as intricate, multi-scale networks where material behavior, process dynamics, and performance outcomes are tightly interlinked.

Understanding behavior starts with uncovering the relationships between process parameters, material properties, and final structure — the process-structure-property relationships. We pursue this through carefully designed experiments, high-resolution characterization, and systematic analysis.

Predicting system behavior requires robust modeling frameworks. We develop hybrid models that combine physics-based and data-driven approaches — physics-informed, data-augmented models that achieve high fidelity and predictive accuracy while staying computationally efficient and physically interpretable.

Controlling real-world variability. Even the best models can’t fully capture real-world uncertainty. We develop intelligent control strategies — novel control frameworks, learning-based adaptation, and real-time decision-making algorithms — that let systems respond and self-correct as conditions change.

Together, our research advances the science of modeling and control for next-generation manufacturing systems and beyond, pushing the boundaries of flexibility, precision, and reliability.

Featured application: Semiconductor manufacturing

Semiconductors power everything from smartphones to vehicles, and demand keeps climbing. Traditional semiconductor manufacturing requires precision at the nanoscale inside expensive, tightly controlled cleanroom facilities. Additive manufacturing (AM) offers a compelling alternative — lower cost, more design freedom, less material waste — but today’s AM processes largely run open-loop, leading to inconsistent results that fall short of semiconductor standards. FRG is working to close that gap: advancing the scientific understanding, enabling technologies, and intelligent control strategies needed to make AM viable for flexible, customized semiconductor fabrication.