Application of robust monotonically convergent spatial iterative learning control to microscale additive manufacturing

Abstract

Microscale additive manufacturing μ-AM processes are a class of manufacturing processes used to fabricate micron-sized structures in a sequence of direct additions of materials as instructed by a digital file, as opposed to the lithographic patterning and subtractive etching used in traditional microscale manufacturing. Despite being sophisticated, numerically controlled tools, material addition is an open-loop process which requires continual user intervention to heuristically tune process parameters. This paper details the first experimental demonstration of a run-to-run feedback algorithm termed Spatial Iterative Learning Control (SILC), a framework previously introduced by the authors to enable robust, auto-regulation of sensitive μ-AM processes [1, 2]. We demonstrate that SILC enables us to autonomously fabricate complex topography structures with as small as 5 µm x- and y-axis resolution and ∼ 113 nm feature height accuracy, without any heuristic tuning by a user. Lastly, it was observed that an SILC design was robust to system faults, as demonstrated by the ability to recover from both an actuator and sensor fault in two iterations.