Volumetric imaging of the intraocular propagation medium using differential OTF wavefront sensing

2013 ◽  
Author(s):  
Johanan L. Codona ◽  
Nathan Doble
Keyword(s):  
Wavefront Sensing ◽  
Volumetric Imaging ◽  
Propagation Medium

Localized Die Metallization Damage Induced During Laser-Marking of a Semiconductor Package

2007 ◽  
Author(s):  
Paul Eric B. Parañal
Keyword(s):  
Laser Beam ◽  
Direct Interaction ◽  
Silica Spheres ◽  
Layer By Layer ◽  
Laser Marking ◽  
Molding Compound ◽  
Molding Material ◽  
Die Surface ◽  
Passivation Layers ◽  
Propagation Medium

Abstract This paper presents a new fail mechanism for laser-marking induced die damage. Discovered during package qualification, silica spheres – commonly used as fillers in the molding material, was shown to act as a propagation medium that promote the direct interaction of the scribing laser beam and the die surface. Critical to the understanding of the fail mechanism is the deprocessing technique devised to allow layer by layer examination of the metallization and passivation layers in an encapsulated silicon die. The technique also made possible the inspection of the molding compound profile directly on top of the affected die area.

Broadband liner impedance eduction for multimodal acoustic propagation in the presence of a mean flow

2016 ◽  
Vol 11 (2) ◽  
pp. 150-155
Author(s):  
R. Troian ◽  
D. Dragna ◽  
C. Bailly ◽  
M.-A. Galland
Keyword(s):  
Numerical Model ◽  
Flow Characteristics ◽  
Acoustic Propagation ◽  
Rational Fraction ◽  
Physical Parameters ◽  
Mean Flow ◽  
Linearized Euler Equations ◽  
Grazing Flow ◽  
Propagation Medium ◽  
Difference Time

Modeling of acoustic propagation in a duct with absorbing treatment is considered. The surface impedance of the treatment is sought in the form of a rational fraction. The numerical model is based on a resolution of the linearized Euler equations by finite difference time domain for the calculation of the acoustic propagation under a grazing flow. Sensitivity analysis of the considered numerical model is performed. The uncertainty of the physical parameters is taken into account to determine the most influential input parameters. The robustness of the solution vis-a-vis changes of the flow characteristics and the propagation medium is studied.

scholarly journals Recurrent neural network-based volumetric fluorescence microscopy

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Luzhe Huang ◽  
Hanlong Chen ◽  
Yilin Luo ◽  
Yair Rivenson ◽  
Aydogan Ozcan
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Fluorescence Microscopy ◽  
Recurrent Network ◽  
Sample Volume ◽  
Depth Of Field ◽  
Objective Lens ◽  
Wide Field ◽  
Volumetric Imaging ◽  
3D Image Reconstruction

AbstractVolumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume. Through a recurrent convolutional neural network, which we term as Recurrent-MZ, 2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field. Using experiments on C. elegans and nanobead samples, Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens, also providing a 30-fold reduction in the number of axial scans required to image the same sample volume. We further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions, including e.g., different sequences of input images, covering various axial permutations and unknown axial positioning errors. We also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input, matching confocal microscopy images of the same sample volume. Recurrent-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework, overcoming the limitations of current 3D scanning microscopy tools.

scholarly journals A Tip–Tilt and Piston Detection Approach for Segmented Telescopes

Photonics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 3
Author(s):  
Shun Qin ◽  
Wai Kin Chan
Keyword(s):  
Error Detection ◽  
Phase Retrieval ◽  
Dynamic Range ◽  
Detection Accuracy ◽  
Wavefront Sensing ◽  
Next Generation ◽  
Large Aperture ◽  
Detection Approach ◽  
Segmented Mirror ◽  
And Control

Accurate segmented mirror wavefront sensing and control is essential for next-generation large aperture telescope system design. In this paper, a direct tip–tilt and piston error detection technique based on model-based phase retrieval with multiple defocused images is proposed for segmented mirror wavefront sensing. In our technique, the tip–tilt and piston error are represented by a basis consisting of three basic plane functions with respect to the x, y, and z axis so that they can be parameterized by the coefficients of these bases; the coefficients then are solved by a non-linear optimization method with the defocus multi-images. Simulation results show that the proposed technique is capable of measuring high dynamic range wavefront error reaching 7λ, while resulting in high detection accuracy. The algorithm is demonstrated as robust to noise by introducing phase parameterization. In comparison, the proposed tip–tilt and piston error detection approach is much easier to implement than many existing methods, which usually introduce extra sensors and devices, as it is a technique based on multiple images. These characteristics make it promising for the application of wavefront sensing and control in next-generation large aperture telescopes.

scholarly journals Computational Method for Wavefront Sensing Based on Transport-Of-Intensity Equation

Photonics ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 177
Author(s):  
Iliya Gritsenko ◽  
Michael Kovalev ◽  
George Krasin ◽  
Matvey Konoplyov ◽  
Nikita Stsepuro
Keyword(s):  
A Priori ◽  
Spherical Wave ◽  
Computational Method ◽  
Optical Systems ◽  
Radius Of Curvature ◽  
Imaging Method ◽  
Wavefront Sensing ◽  
Phase Imaging ◽  
Transport Of Intensity Equation ◽  
Priori Information

Recently the transport-of-intensity equation as a phase imaging method turned out as an effective microscopy method that does not require the use of high-resolution optical systems and a priori information about the object. In this paper we propose a mathematical model that adapts the transport-of-intensity equation for the purpose of wavefront sensing of the given light wave. The analysis of the influence of the longitudinal displacement z and the step between intensity distributions measurements on the error in determining the wavefront radius of curvature of a spherical wave is carried out. The proposed method is compared with the traditional Shack–Hartmann method and the method based on computer-generated Fourier holograms. Numerical simulation showed that the proposed method allows measurement of the wavefront radius of curvature with radius of 40 mm and with accuracy of ~200 μm.

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