SLIDERS: the next generation of automated optical design tools has arrived

2003 ◽  
Author(s):  
Joseph M. Howard
Keyword(s):  
Optical Design ◽  
Design Tools ◽  
Next Generation

The optical design of GMOX: a next-generation instrument concept for Gemini

2016 ◽  
Author(s):  
Robert Barkhouser ◽  
Massimo Robberto ◽  
Stephen A. Smee ◽  
Zoran Ninkov ◽  
Mario Gennaro ◽  
...  
Keyword(s):  
Optical Design ◽  
Next Generation

Differentiable Compound Optics and Processing Pipeline Optimization for End-to-end Camera Design

2021 ◽  
Vol 40 (2) ◽  
pp. 1-19
Author(s):  
Ethan Tseng ◽  
Ali Mosleh ◽  
Fahim Mannan ◽  
Karl St-Arnaud ◽  
Avinash Sharma ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Optical Design ◽  
Neural Nets ◽  
Fine Tuning ◽  
Optical Systems ◽  
Design Tools ◽  
End To End ◽  
Application Specific ◽  
Network Processing

Most modern commodity imaging systems we use directly for photography—or indirectly rely on for downstream applications—employ optical systems of multiple lenses that must balance deviations from perfect optics, manufacturing constraints, tolerances, cost, and footprint. Although optical designs often have complex interactions with downstream image processing or analysis tasks, today’s compound optics are designed in isolation from these interactions. Existing optical design tools aim to minimize optical aberrations, such as deviations from Gauss’ linear model of optics, instead of application-specific losses, precluding joint optimization with hardware image signal processing (ISP) and highly parameterized neural network processing. In this article, we propose an optimization method for compound optics that lifts these limitations. We optimize entire lens systems jointly with hardware and software image processing pipelines, downstream neural network processing, and application-specific end-to-end losses. To this end, we propose a learned, differentiable forward model for compound optics and an alternating proximal optimization method that handles function compositions with highly varying parameter dimensions for optics, hardware ISP, and neural nets. Our method integrates seamlessly atop existing optical design tools, such as Zemax . We can thus assess our method across many camera system designs and end-to-end applications. We validate our approach in an automotive camera optics setting—together with hardware ISP post processing and detection—outperforming classical optics designs for automotive object detection and traffic light state detection. For human viewing tasks, we optimize optics and processing pipelines for dynamic outdoor scenarios and dynamic low-light imaging. We outperform existing compartmentalized design or fine-tuning methods qualitatively and quantitatively, across all domain-specific applications tested.

RapidNano: Towards 20nm Particle Detection on EUV Mask Blanks

MRS Advances ◽  
2016 ◽  
Vol 1 (31) ◽  
pp. 2225-2236 ◽  
Author(s):  
Jacques van der Donck ◽  
Peter Bussink ◽  
Erik Fritz ◽  
Peter van der Walle
Keyword(s):  
Particle Size ◽  
Signal To Noise Ratio ◽  
Semiconductor Industry ◽  
Optical Design ◽  
Dark Field ◽  
Detection Methods ◽  
Next Generation ◽  
Large Area ◽  
Dark Field Imaging ◽  
20 Nm

ABSTRACTCleanliness is a prerequisite for obtaining economically feasible yield levels in the semiconductor industry. For the next generation of lithographic equipment, EUV lithography, the size of yield-loss inducing particles for the masks will be smaller than 20 nm. Consequently, equipment for handling EUV masks should not add particles larger than 20 nm. Detection methods for 20 nm particles on large area surfaces are needed to qualify the equipment for cleanliness. Detection of 20 nm particles is extremely challenging, not only because of the particle size, but also because of the large surface area and limited available time.In 2002 TNO developed the RapidNano, a platform that is capable of detecting nanoparticles on flat substrates. Over the last decade, the smallest detectable particle size was decreased while the inspection rate was increased. This effort has led to a stable and affordable detection platform that is capable of inspecting the full surface of a mask blank.The core of RapidNano is a dark-field imaging technique. Every substrate type has a typical background characteristic, which strongly affects the size of the smallest detectable particle. The noise level is induced by the speckle generated by the surface roughness of the mask. The signal-to-noise ratio can be improved by illuminating the inspection area from nine different angles. This improvement was first shown on test bench level and then applied in the RapidNano3. The RapidNano3 is capable of detecting 42nm latex sphere equivalents (and larger) on silicon surfaces. RapidNano4, the next generation, will use 193 nm light and the same nine angle illumination mode. Camera sensitivity and available laser power determine the achievable throughput. Therefore, special care was given to the optical design, particularly the optical path. With RapidNano4, TNO will push the detection limit of defects on EUV blanks to below 20nm.

Lessons learned in the optical design of the Next-Generation Space Telescope

1998 ◽  
Author(s):  
James B. Hadaway ◽  
Mark E. Wilson ◽  
David C. Redding ◽  
Robert A. Woodruff
Keyword(s):  
Optical Design ◽  
Lessons Learned ◽  
Next Generation ◽  
Space Telescope

A Model for Information Flow in Design

2000 ◽  
Author(s):  
Steven B. Shooter ◽  
Walid T. Keirouz ◽  
Simon Szykman ◽  
Steven Fenves
Keyword(s):  
Information Flow ◽  
Information Exchange ◽  
Computer Aided Design ◽  
Design Tools ◽  
Next Generation ◽  
Levels Of Abstraction ◽  
Design Information ◽  
Storage And Retrieval ◽  
Computer Aided ◽  
Aided Design

Abstract The complexity of modern products and design tools has complicated the exchange of design information. It is widely recognized that the capture, storage, and retrieval of design information is one of the major challenges for the next generation of computer aided design tools. This paper presents a model for the flow of design information that supports a semantics-based approach for developing information exchange standards. The model classifies design information into various types, organizes these types into information states and levels of abstraction, and identifies the various transformations that operate between the information states. The model is then applied to an example of a transmission for a cordless drill.

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