Micro- and nanoscale patterning of conformal thin-film coatings on the exterior surfaces of complex three-dimensional (3D) structures is essential for emerging applications such as soft robotics, photonics, and functional 3D-printed MEMS devices. However, existing methods struggle to deliver high-resolution patterning on complex 3D structures and often suffer from poor thickness control, and inadequate surface conformity of the thin-film coatings. Here we present a robust approach for patterning of conformal thin-film coatings on complex 3D structures, including on sloped surfaces with angles up to 90°, with multiscale dimensions from 100 μm to 100 nm, and even down to the sub-30 nm scale when mask shrinkage techniques are used. This patterning approach utilizes a lithographically defined 3D Scaffold-Architected Lift-Off (SALO) mask in the lift-off process. It is agnostic to the used thin-film deposition process and enables even lift-off patterning of atomic layer deposited (ALD) conformal coatings, a task infeasible for conventional shadowing-based lift-off processes. Our approach opens opportunities for manufacturing complex 3D structures at the micro- and nanoscale by enabling lithographic patterning on the exterior surfaces of arbitrary 3D structures.

Tactile sensors play a crucial role in enhancing the integration of automation, robotics, and biomedical equipment, particularly in perceptual functions. Optical fiber-based tactile sensors have gained significance due to their robustness and immunity to electromagnetic interference. However, existing optical fiber-based tactile sensors face limitations related to bio-imitation, scalability, and precise data processing algorithms. This study introduces a novel skin-inspired polydimethylsiloxane (PDMS)-manufactured tactile sensor utilizing a structured light source with low-cost light-emitting diodes and a multimode optical fiber, coupled with tactile information processing through a trained convolutional neural network (CNN). Specklegram images captured from the optical fiber are analyzed for force amplitude and tactile location. The CNN is trained, validated, and tested, achieving accuracies of 99.6%, 99.5%, and 99%, respectively. The tactile sensor demonstrates a spatial resolution of 2 mm and a force-sensing range up to 3 N. The confusion matrix, based on classification results, reveals only three misclassifications out of 315 tests, indicating a mean absolute error (MAE) of 0.95%. The spatial resolution and force-sensing capabilities, coupled with the machine learning approach of the proposed tactile sensor, showcase promising potential for future applications in tactile embodiment.

Specklegram-based optical fiber sensors have gained attention for their advantages of sensitivity, low cost, and intelligent sensing ability in fields such as force, small deflection, single-point temperature sensing, and so on. To enhance specklegram-based distributive temperature sensing through a multimode optical fiber (MMF), this work proposes a quasi-distributed approach involving a triple-color illumination with light emitting diodes (LEDs) and a hybrid model that combines principal component analysis and variational autoencoder (PCAVAE). The proposed MMF optical sensor demonstrates strong performance in sensing the distributive temperature configurations of four heaters, showcasing its capability in detecting quasi-distributed temperature information in the range of 30 degrees C-60 degrees C, achieving a spatial resolution of 3 cm and a temperature resolution of 1 degrees C with a mean absolute error (MAE) of 1.85 degrees C and a root mean square error (RMSE) of 2.63,degrees C respectively. The proposed sensor opens a gateway for exploiting multiple sites sensing capability with a single optical fiber specklegram and exhibits application potentials for low-cost quasi-distributed optical temperature detection.

Imaging through the multi-mode fiber (MMF) becomes an attractive approach for gaining visual access to confined spaces. However, current imaging techniques through a MMF still encounter challenges including modal dispersion, complex wave-front shaping mechanism, and expensive light sources and modulations. This work proposed a cost-efficient setup with three light-emitting diodes as the illumination light source (including red, green, and blue light) and a hybrid model including the principal component analysis and a variational auto-encoder (PCAVAE) for reconstructing the transmitted images. The reconstructed images demonstrate high fidelity compared with their ground truth images. The average similarity index value of the reconstructed images is as high as 0.99. Experimental works indicated that the proposed approach was capable of rejecting 10% white noise in the imaging process. The proposed triple-color illumination method paves a cost-effective way of transmitting images through an MMF. The PCAVAE model established in this work demonstrates great potential for processing scrambled images transmitted by the MMF.