In optomechanical sensing, mechanical oscillators respond to external forces, magnetic and acoustic signals, while optical probes read out the motion of mechanical oscillators to infer the external signal. Researchers can use an array of optomechanical sensors to scale up measurement performance, but the measurement precision of optomechanical sensors is fundamentally limited by the measurement noise arising from quantum fluctuations of the probe light and mechanical oscillators. Zhang’s team is currently working on integrated quantum chips for entanglement-enhanced sensing. Assembling all of these components on-chip will eliminate the need for optical alignment and make the sensors much more stable—and overcome the two main challenges of their current experiment. Zhang’s talk on quantum sensing will cover some of the fundamental concepts and their recent research in this area.

Production scale testing of silicon photonic devices continues to be a challenge due to the multi degree of freedom, high precision optical alignments required for wafer and die level testing. Wide variances in chip designs and coupling features complicate test procedures making it difficult to identify a system capable of producing repeatable measurements across various topologies. This webinar is intended to serve as a guide for selecting precision motion equipment to minimize the impact of positioning errors on your optical alignment test results. First, terminology typically used in the precision automation industry and their relationships to optical alignments will be introduced. Next, fundamental principles of motion control and their impact on alignment quality will be discussed. Finally, a case study on error motions induced by 6 degree of freedom positioning devices and their impact on optical alignments will be presented to illustrate the importance of selecting the optimal positioning equipment for a given alignment application.
 

Multiphysics simulation plays an important role in driving faster and more cost-effective research and development (R&D) of optical components, which can vary in size from subwavelength range to optically large devices.

COMSOL Multiphysics® offers unique multiphysics modeling capabilities, which enables users to predict and optimize these couplings, exploiting the benefits to develop superior product ideas or suppress unwanted effects. In addition, with its built-in Application Builder and Model Manager, COMSOL Multiphysics® provides an ecosystem of functionality that facilitates collaboration across departments as well as structured and lean management of simulation data.

In this session, you will learn about current optics and photonics modeling trends at leading research institutes and R&D departments within the industry. You will see how these trends are inspired or enabled by multiphysics modeling and simulation apps.

Compact, flat metalenses are emerging as viable alternatives to traditional bulky refractive lenses for use in industrial and scientific applications, such as cell phone cameras, CMOS imaging sensors, spectrometers, and augmented reality systems. Designing metalens systems, however, typically requires deep physics knowledge and significant design experience. MetaOptic Designer, a fully automated technology developed by Synopsys, has dramatically simplified metalens design complexity. With minimum inputs required, designers at all levels of expertise can create novel metalens designs quickly and easily. Based on market demand and feedback, significant improvements have been made in MetaOptic Designer since its initial release in 2022. Join the session to learn more about this groundbreaking metalens design tool.

High-throughput metrology control of optical components is critical for lens development, process optimization, and mass production.

In this TechTalk it will be discussed how white light interferometry (WLI) has the capability to encompass several optical components in a large field of view while maintaining subnanometer height resolution and micron-level lateral resolution. WLI is an ideal optical measurement technique for characterizing these types of surfaces. Bruker’s industry-leading WLI solutions combine automation and on-the-fly analysis to enable comprehensive reports on lens parameters. This TechTalk also covers curvature analysis through the Zernike coefficient, defect monitoring and deviation from ideal aspheric shape. 

For UV, EUV, and X-ray wavelength applications, optics must be extremely smooth to avoid scattering, which affects image quality, signal-to-noise, and other critical optical performance properties. The typically high incident energy can also degrade optics quickly.  In order to ensure sub-nanometer level smoothness for optical performance, a reliable, reproducible measurement method is critical.  But, while newer manufacturing techniques have enabled production of supersmooth optics, the metrology to accurately measure these surfaces has lagged behind.

This presentation will discuss some of the challenges of measuring sub-nanometer level surface roughness and compare the different measurement techniques for these types of measurements available today.  It will also introduce a novel vibration-insensitive measurement technique that has demonstrated advanced capabilities in measuring super-smooth optics, especially in noisy environments.

Last, the talk will discuss different factors to consider while choosing a technology to measure super smooth optics, such as spatial requirements, repeatability, size of the optic, and environmental considerations such as vibration, turbulence, and automation requirements.

Deep learning techniques create new opportunities to revolutionize tissue staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, accurate and environmentally friendly alternatives to standard chemical staining methods. These deep learning-based virtual staining techniques can successfully generate different types of histological stains, including immunohistochemical stains, from label-free microscopic images of unstained samples by using, e.g., autofluorescence microscopy, quantitative phase imaging (QPI) and reflectance confocal microscopy. Similar approaches were also demonstrated for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this presentation, I will provide an overview of our recent work on the use of deep neural networks for label-free tissue staining, also covering their biomedical applications