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

Upsampling01:22

Upsampling

744
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
744
Reducing Line Loss01:18

Reducing Line Loss

523
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
523
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

16.0K
Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
16.0K

You might also read

Related Articles

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

Sort by
Same author

HyperNATE: Scaling tensor-based hypergraph neural networks through attention.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Focal spot mitigation with dynamic sampling conditions for high-resolution X-ray CT via diffusion priors.

Optics express·2026
Same author

Cascade deep polarization network for precise image semantic segmentation.

Applied optics·2025
Same author

Information theoretical computational lithography based on pattern density statistics.

Optics express·2025
Same author

Fast alternating minimization algorithm for coded aperture snapshot spectral imaging based on sparsity and deep image priors.

Applied optics·2025
Same author

Design of on-chip coded X-ray sensors for high-confidence super-resolution radiological imaging.

Applied optics·2025
Same journal

Long-term stabilization of intensity-difference squeezing from four-wave mixing in rubidium vapor.

Optics express·2026
Same journal

Robust 3D topography measurement of large-range high-aspect-ratio structures based on dual-domain statistical filtering in SD-OCT.

Optics express·2026
Same journal

Broadband transmissive terahertz metasurface for simultaneous quad-mode OAM multiplexing.

Optics express·2026
Same journal

Leveraging two-dimensional materials for high-sensitivity optical sensors: quasi-bound states in the continuum within hybrid metasurfaces.

Optics express·2026
Same journal

Resolution investigation for dual-spherical-wave optical scanning holographic microscopy: methods and performance.

Optics express·2026
Same journal

Robustness of parallel subnetwork-filtered diffractive deep neural networks.

Optics express·2026
See all related articles

Related Experiment Video

Updated: Apr 27, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K

Inverse lithography source optimization via compressive sensing.

Zhiyang Song, Xu Ma, Jie Gao

    Optics Express
    |July 1, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel source optimization (SO) method using compressive sensing (CS) to enhance lithographic imaging. The new approach efficiently extends the process window (PW) for improved manufacturing.

    More Related Videos

    Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
    10:53

    Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

    Published on: January 3, 2017

    10.9K
    Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
    07:47

    Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

    Published on: February 12, 2017

    6.7K

    Related Experiment Videos

    Last Updated: Apr 27, 2026

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.6K
    Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks
    10:53

    Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

    Published on: January 3, 2017

    10.9K
    Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
    07:47

    Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography

    Published on: February 12, 2017

    6.7K

    Area of Science:

    • Computational lithography
    • Image processing and reconstruction

    Background:

    • Current source optimization (SO) methods in lithography are computationally intensive or offer limited process window (PW) extension.
    • Pixel-based SO approaches often involve solving complex quadratic or linear programming problems.

    Purpose of the Study:

    • To develop an efficient and robust source optimization method for improving lithographic imaging.
    • To leverage compressive sensing (CS) theory for accelerated and enhanced SO design.

    Main Methods:

    • Formulating source optimization as an underdetermined linear problem, enabling acceleration.
    • Transforming the SO problem into an l1-norm image reconstruction problem based on CS principles.
    • Applying the linearized Bregman algorithm to synthesize sparse optimal source patterns.

    Main Results:

    • The proposed linear SO formulation demonstrates superior aerial image contrast compared to traditional quadratic methods.
    • Sparse regularization in inverse lithography effectively extends the process window (PW) of lithography systems.
    • Simulations confirm the superiority of the proposed SO method over existing approaches.

    Conclusions:

    • Compressive sensing provides an efficient and robust framework for source optimization in lithography.
    • The developed method enhances manufacturability and extends the process window, improving lithographic imaging performance.