Efficient Fabrication of High-Aspect-Ratio Stepped-End Microneedles via Vision-Guided Assembly for Flexible Microelectrode Implantation
Xianghe Meng, Xingjian Shen, Yan Xu, Hui Xie
Abstract
This paper presents an automated assembly method for the efficient and cost-effective fabrication of high-aspect-ratio stepped-end (HARSE) microneedles for flexible neural microelectrode implantation. The core process is a novel variable stiffness insertion (VSI) method, employing a vision-guided system to assemble commercially available tungsten wires and stainless steel tubes with microscale diameters into HARSE structures. The VSI system uses a five-axis adjustment stage, gripping module, and dual-camera feedback. A finite state machine model guides the assembly process through low-stiffness and high-stiffness feeding loops, ensuring accurate construction of the stepped-end structure. Pre-processing (wire straightening) and post-processing (tip etching) steps complement the VSI process. Fabricated microneedles demonstrated precise microelectrode grasping with less than 32 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\bm {\mu }$</tex-math></inline-formula>m positional shift, approximately the size of a single neuron, and required 2.3 times less force for dura mater penetration compared to flat-ended needles. With an average manufacturing time under 4 minutes per needle and use of cost-effective equipment, this method significantly reduces fabrication costs and improves efficiency compared to techniques using expensive equipment like focused ion beam or femtosecond laser machining, addressing the growing demand for implantable flexible microelectrodes in neuroscience research and brain-machine interface development.
BibTeX
@inproceedings{ral2025_efficientfabrica,
title = {Efficient Fabrication of High-Aspect-Ratio Stepped-End Microneedles via Vision-Guided Assembly for Flexible Microelectrode Implantation},
author = {Xianghe Meng and Xingjian Shen and Yan Xu and Hui Xie},
booktitle = {RA-L 2025},
year = {2025}
}