Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cost-effectiveness of concurrently available oral and long-acting injectable pre-exposure prophylaxis for preventing HIV infections among men who have sex with men in eastern China: A modelling study.

BMC public health·2026
Same author

Dietary Sodium Lactate Alleviates Ammonia Stress-Induced Growth Impairment, Oxidative Damage and Metabolic Disorder in Juvenile Yellow Catfish.

Aquaculture nutrition·2026
Same author

Capacitively Coupled Alternating Electric Field for Accelerated and General Synthesis of Metal-Organic Frameworks.

ACS central science·2026
Same author

Physiological and Intestinal Microbiota Responses to the Feeding Stimulant Dimethyl-β-Propiothetin (DMPT) in Aquatic Animals-A Preliminary Study on <i>Pontastacus leptodactylus</i> Fed on a Plant-Based Diet.

Antioxidants (Basel, Switzerland)·2026
Same author

Chemoprophylaxis effect of EGCG on various digestive system diseases: a systematic review and meta-analysis.

Frontiers in medicine·2026
Same author

Synergistic toxicity of abamectin with nanoplastics in rainbow trout mediated by gut-liver axis disruption: Insights into oxidative stress, metabolic dysregulation, and microbiota change.

Pesticide biochemistry and physiology·2026

Related Experiment Video

Updated: Nov 12, 2025

Laser-induced Forward Transfer of Ag Nanopaste
08:07

Laser-induced Forward Transfer of Ag Nanopaste

Published on: March 31, 2016

11.6K

U-Net convolutional neural network-based modification method for precise fabrication of three-dimensional

Xiuhui Sun, Shaoyun Yin, Haibo Jiang

    Optics Express
    |March 17, 2021
    PubMed
    Summary

    This study introduces a U-Net convolutional neural network method to enhance the precision of 3D microstructures fabricated with laser direct writing lithography (LDWL). The approach significantly improves fabrication accuracy for micro-lenses, leading to better laser beam shaping.

    More Related Videos

    Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
    07:38

    Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

    Published on: June 7, 2024

    1.9K
    Control of Cell Geometry through Infrared Laser Assisted Micropatterning
    11:04

    Control of Cell Geometry through Infrared Laser Assisted Micropatterning

    Published on: July 10, 2021

    3.6K

    Related Experiment Videos

    Last Updated: Nov 12, 2025

    Laser-induced Forward Transfer of Ag Nanopaste
    08:07

    Laser-induced Forward Transfer of Ag Nanopaste

    Published on: March 31, 2016

    11.6K
    Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
    07:38

    Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

    Published on: June 7, 2024

    1.9K
    Control of Cell Geometry through Infrared Laser Assisted Micropatterning
    11:04

    Control of Cell Geometry through Infrared Laser Assisted Micropatterning

    Published on: July 10, 2021

    3.6K

    Area of Science:

    • Materials Science
    • Optical Engineering
    • Computational Science

    Background:

    • Precise fabrication of 3D microstructures is crucial for advanced optical applications.
    • Laser Direct Writing Lithography (LDWL) is a key technique for microfabrication.
    • Improving the accuracy of LDWL remains a challenge.

    Purpose of the Study:

    • To develop a novel method for enhancing the fabrication precision of 3D microstructures using LDWL.
    • To establish accurate mapping between design data and fabricated microstructures.
    • To improve the performance of micro-optical elements fabricated by LDWL.

    Main Methods:

    • A U-Net convolutional neural network was employed to learn the mapping between exposure intensity data and surface profile data.
    • The network was trained using data from LDWL systems.
    • The learned mapping was used to modify exposure intensity data for micro-lens fabrication.

    Main Results:

    • Significant improvements in fabrication precision were demonstrated for parabolic and saddle concave micro-lenses.
    • Mean Squared Error (MSE) decreased from 100 to 17 for parabolic and 151 to 50 for saddle concave micro-lenses.
    • Peak Signal-to-Noise Ratio (PSNR) increased from 22dB to 29dB and 20dB to 25dB, respectively.
    • Enhanced laser beam shaping performance was observed.

    Conclusions:

    • The U-Net based modification method offers a new solution for high-precision 3D microstructure fabrication via LDWL.
    • This approach effectively bridges the gap between designed and actual microstructures.
    • The improved micro-lenses show enhanced functionality in laser beam shaping.