Research on Path Planning for Meniscus-Confined Electrodeposition on Non-Ideal Substrates with Global Inclination
Year: 2026
Type: article
Abstract: To address the challenges associated with pattern deposition via meniscus-confined electrodeposition (MCED) on nominal-flat planar substrates with global tilt, including large variations in probe–substrate spacing, meniscus instability, and poor printing continuity caused by substrate inclination, a hybrid path planning algorithm, termed Z-axis-weighted adaptive partitioning and traveling salesman-based heuristic path optimization, is proposed. The algorithm integrates Z-axis-weighted adaptive partitioning with a traveling salesman problem (TSP) heuristic strategy, using the stable meniscus height as the partitioning criterion. Using the preset deposition pattern as input, this method extracts pattern boundaries, completes regional segmentation, fits the spatial plane of the substrate from four-point coordinates, and accurately maps two-dimensional (2D) image coordinates to three-dimensional space. The number of partitions is automatically determined under the constraint of the allowable height difference for a stable meniscus. After implementing Z-axis-weighted adaptive partitioning, a greedy nearest-neighbor TSP heuristic is applied to obtain a heuristically-optimized deposition sequence for each partition, reducing the path-switching time and improving the printing process stability. The experimental results demonstrate that the proposed method can achieve precision 2D pattern deposition of a 60 μm × 60 μm feature within an 70 μm × 70 μm planar area on conductive substrates with uncertain flatness. The printed trajectories are uniform with distinct line contours, and the surface roughness of deposited patterns is no more than 0.05 μm, indicating stable and reliable forming quality.
Source: Materials
DOI: 10.3390/ma19204312
Institution: Nantong University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G9001174095
OpenAlex ID: W7221671035
