Python in Nanophotonics Research

Bienstman, Peter; Vanholme, Lieven; Booafrts, W.; Dumon, Pieter; Vandersteegen, Peter

doi:10.1109/mcse.2007.59

Cited by 10 publications

(10 citation statements)

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“…At that point, the decision was made to implement this tool as a scripting framework in Python, because of the flexibility and readability of the language and the availability of many high-performance scientific libraries. 21 With IPKISS, the photonics research group has generated hundreds of designs of silicon photonic integrated circuits, and a large library of parametric building blocks was constructed, together with the layer settings to generate complex GDII files. Since then, IPKISS has evolved from a flexible GDSII generator to a broader component-oriented design framework.…”

Section: Ipkiss: a Parametric Design Frameworkmentioning

confidence: 99%

Integrated design for integrated photonics: from the physical to the circuit level and back

Bogaerts

Pathak

et al. 2013

Integrated Optics: Physics and Simulations

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Section: Ipkiss: a Parametric Design Frameworkmentioning

confidence: 99%

Integrated design for integrated photonics: from the physical to the circuit level and back

Bogaerts

Pathak

et al. 2013

Integrated Optics: Physics and Simulations

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“…This gives the designer an easy entry point, and access to a wealth of scientific and engineering libraries [3]. Also, Python is easy to interface with third-party tools.…”

Section: Our Approachmentioning

confidence: 99%

Silicon photonics integrated design

Bogaerts

Dumon

Fiers

et al. 2012

International Conference on Fibre Optics and Photonics

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Silicon photonics is a fast-growing technology, but the design capability for large-scale photonic-electronic circuits is lacking. Challenges include signal handling, variability and photonic-electronic co-integration. We will discuss these and the tools we are developing.OCIS codes: (130.0130) Integrated optics, (220.0220) Optical design and fabrication IntroductionSilicon photonics is a field of photonics with a rapidly growing industrial interest, with potential applications in communication and sensors. The key strengths of the technology are twofold: silicon is compatible with the manufacturing technology for CMOS, leveraging a huge technology knowledge base [1]. The second strength of silicon photonics is its high refractive index contrast. Silicon and silicon dioxide have an index contrast of more than 2-to-1, which allows submicron waveguides and bends of a few micrometer: Silicon photonic circuits can be made orders-of-magnitude smaller than their low-contrast counterparts. Not just smaller, but more complex: thousands to millions of components can be integrated on a chip. Most optical functions have already been demonstrated in silicon with industrial processes, and the potential to integrate electronics makes it even more attractive.This powerful technology introduces some considerable design challenges. First of all, the high index contrast makes the structures very sensitive to geometrical variations. In the case of wavelength filters, 1nm-scale variations could render a device useless. Designing circuits with tolerances to compensate for variability in the fabrication will be essential to silicon photonics design. Also, complexity itself poses challenges. Simulating circuits with thousands of components is beyond most of today's tools. Most tools are focused on physical component design, and are mostly disconnected from circuit simulators. Also, the photonics will often need cosimulation with electronics. As the physics are very different, this cosimulation requires new tools that can handle the different concepts together.In this paper and presentation, we will discuss these challenges. The solutions are far from complete, and there is not a single tool which will solve all problems. Our own methods use a scripting framework called IPKISS [2] which revolves around a single component definition and brings together tools (even from different vendors) to enable a design flow including physical simulation, circuit synthesis, mask layout and even testing procedures.

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“…At UGent/IMEC, we have developed a litho mask design toolkit for silicon photonics in pure Python. Addon tools and libraries have been developed for electromagnetic modeling, design optimization [5] and process simulation [6]. The long-term goal is to further automate closed-loop optimization of photonic circuits [7].…”

Section: The Use Of Python In Our Researchmentioning

confidence: 99%