SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.

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SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

An overview is presented of the current state-of-the-art in silicon nanophotonic ring resonators. Basic theory of ring resonators is discussed, and applied to the peculiarities of submicron silicon photonic wire waveguides: the small dimensions and tight bend radii, sensitivity to perturbations and the boundary conditions of the fabrication processes. Theory is compared to quantitative measurements. Finally, several of the more promising applications of silicon ring resonators are discussed: filters and optical delay lines, label-free biosensors, and active rings for efficient modulators and even light sources.

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Silicon microring resonators

Part I. Silicon Photonics - Introduction: 1. Fabless Silicon Photonics: 1.1 Introduction 1.2 Silicon photonics - the next fabless semiconductor industry 1.3 Applications 1.4 Technical challenges and the state of the art 1.5 Opportunities 2. Modelling and Design Approaches: 2.1 Optical Waveguide Mode Solver 2.2 Wave Propagation 2.3 Optoelectronic models 2.4 Microwave Modelling 2.5 Thermal Modelling 2.6 Photonic Circuit Modelling 2.7 Physical Layout 2.8 Software Tools Integration Part II. Silicon Photonics - Passive Components: 3. Optical Materials and Waveguides: 3.1 Silicon-on-Insulator 3.2 Waveguides 3.3 Bent waveguides 3.4 Code Listings 3.5 Problems 4. Fundamental Building Blocks: 4.1 Directional couplers 4.2 Y-Branch 4.3 Mach-Zehnder Interferometer 4.4 Ring resonators 4.5 Waveguide Bragg Grating Filters 4.6 Code Listings 4.7 Problems 5. Optical I/O: 5.1 The challenge of optical coupling to silicon photonic chips 5.2 Grating Coupler 5.3 Edge Coupler 5.4 Polarization 5.5 Code Listings 5.6 Problems Part III. Silicon Photonics - Active Components: 6. Modulators: 6.1 Plasma Dispersion E 6.2 PN Junction Phase Shifter 6.3 Micro-ring Modulators 6.4 Forward-biased PIN Junction 6.5 Active Tuning 6.6 Thermo-Optic Switch 6.7 Code Listings 6.8 Problems 7. Detectors: 7.1 Performance Parameters 7.2 Fabrication 7.3 Types of detectors 7.4 Design Considerations 7.5 Detector modelling 7.5.2 Electronic Simulations 7.6 Code Listings 7.7 Problems 8. Lasers: 8.1 External Lasers 8.2 Laser Modelling 8.3 Co-Packaging 8.4 Hybrid Silicon Lasers 8.5 Monolithic Lasers 8.6 Alternative Light Sources 8.7 Problems Part IV. Silicon Photonics - System Design: 9. Photonic Circuit Modelling: 9.1 Need for photonic circuit modelling 9.2 Components for System Design 9.3 Compact Models 9.4 Directional Coupler - Compact Model 9.5 Ring Modulator - Circuit Model 9.6 Grating Coupler - S Parameters 9.7 Code Listings 10. Tools and Techniques: 10.1 Process Design Kit (PDK) 10.2 Mask Layout 11. Fabrication: 11.1 Fabrication Non-Uniformity 11.2 Problems 12. Testing and Packaging: 12.1 Electrical and Optical Interfacing 12.2 Automated Optical Probe Stations 12.3 Design for Test 13. Silicon Photonic System Example: 13.1 Wavelength Division Multiplexed Transmitter.

Silicon Photonics Design: From Devices to Systems

Silicon Photonics technology is rapidly maturing as a platform for larger-scale photonic circuits. As a result, the associated design methodologies are also evolving from componentoriented design to a more circuit-oriented design flow, that makes abstraction from the very detailed geometry and enables design on a larger scale. In this paper, we review the state of this emerging photonic circuit design flow and its synergies with electronic design automation (EDA). We cover the design flow from schematic capture, circuit simulation, layout and verification. We discuss the similarities and the differences between photonic and electronic design, and the challenges and opportunities that present themselves in the new photonic design landscape, such as variability analysis, photonic-electronic co-simulation and compact model definition. Silicon Photonics Circuit Design: Methods, Tools and

https://www.onlinelibrary.wiley.com/doi/epdf/10.1002/lpor.201700237

Silicon Photonics Circuit Design: Methods, Tools and Challenges

We demonstrate compact, broadband, ultralow loss silicon waveguide crossings operating at 1550 nm and 1310 nm. Cross-wafer measurement of 30 dies shows transmission insertion loss of − 0.028 ± 0.009 dB for the 1550 nm device and − 0.017 ± 0.005 dB for the 1310 nm device. Both crossings show crosstalk lower than − 37 dB. The devices were fabricated in a CMOS-compatible process using 248 nm optical lithography with a single etch step.

Ultralow loss single layer submicron silicon waveguide crossing for SOI optical interconnect

We present a method for component-centric modeling of silicon nanophotonics, where a closed optimization loop allows to take the effects of the fabrication process into account during the design of nanophotonic components. This enables black-box component descriptions with functional parameters. Underlying mask layouts of the components can then automatically be optimized for their actual performance, and not just for their geometric layout. To simulate the effect of fabrication, we developed a projection lithography simulator which was included inside the optimization loop. This method was applied to the design of a 1-dimensional distributed Bragg mirror.

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Closed-loop Modeling of Silicon Nanophotonics: From Design to Fabrication and Back Again

The authors describe how they use Python for nanophotonics research-specifically, they describe using it for electromagnetic modeling, mask design, and process simulation

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Python in Nanophotonics Research

Progress and take-up of photonic integration is hampered by a too large variety in devices and technologies. Generic
integration platforms, which support a broad range of applications and can be made accessible through foundries, can
deliver the cost reduction needed for a broader take-up. Silicon is ideally suited as such a generic integration platform
and can support large-scale photonic integrated circuits. The ePIXnet Silicon Photonics Platform was set up to offer
access to high-end silicon facilities for research and prototyping and to help establish photonic foundry processes on the
longer term, bridging the gap between research and the market.

Building technology platforms and foundries for photonic integrated circuits in Europe

ePIXnet is a Network of Excellence with a focus on photonic integrated circuits. Its objective is to serve the academic and industrial research community by building a world-class research infrastructure and to make it accessible. This is done through the creation of technology platforms.

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Research integration and technology platforms in the Network of Excellence ePIXnet on photonic integration

Photonic crystals have received much attention lately because they are very interesting candidates for the realization of photonic integrated circuits in view of their ability to confine light, guide it around tight bends and perform a wealth of other optical functionality like filtering and add-drop multiplexing [1]. These nanophotonics devices are quite challenging to model because of their large index contrast and small dimensions. A full-vectorial model without approximations is therefore required. Candidates for this are the well-known FDTD or finite-difference time-domain method [2], but this approach is relatively brute-force and therefore quite computationally intensive. Another well-established model is plane-wave expansion [3], but it uses a super cell approach and has therefore difficulty in modeling finite devices. The model we present here is based on vectorial eigenmode expansion [4]. We will show how this method is much less brute-force and can be used to handle infinite, finite and semi-infinite structures. It is also freely available from http://camfr.sourceforge.net.

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Lieven Vanholme

Papers

Closed-loop Modeling of Silicon Nanophotonics: From Design to Fabrication and Back Again

Python in Nanophotonics Research

Building technology platforms and foundries for photonic integrated circuits in Europe

Research integration and technology platforms in the Network of Excellence ePIXnet on photonic integration

Eigenmode modeling of photonic crystals devices