Intelligent Field-Adaptive Electrohydrodynamic Printing for Curved Conformal Electronics.

Curved conformal electronics are increasingly important for wearable devices, biosensing, and advanced communication systems, requiring high-resolution and high-precision manufacturing on complex surfaces. Electrohydrodynamic printing is a promising method for fabricating conformal electronics, but its application on curved insulating substrates is hindered by local electric-field disturbances arising from residual charge accumulation and spatial variations, leading to poor dimensional control. To address this challenge, an intelligent field-adaptive electrohydrodynamic printing framework is established, in which alternating-electric-field modulation reduces the effects of residual charge, the equivalent electrical gap quantitatively characterizes spatially varying electric-field conditions, and machine learning-based parameter regulation compensates for local electric-field variations. Based on this framework, a machine-learning and alternating-electric-field synergistic modulated electrohydrodynamic printing approach is developed to achieve stable printing and precise line-width control on curved substrates. The approach rapidly optimizes parameters for target line widths, achieving average relative errors around 10% or lower at the initial printing position, and further reduces average relative line-width errors by up to 7.24 percentage points through adaptive regulation during curved-substrate printing. Diverse high-precision patterns are printed, and the conformal frequency-selective surface and antenna with excellent electromagnetic performance are fabricated, demonstrating the potential of this framework for advancing curved conformal electronics manufacturing.