🌟 𝗜𝗻𝘃𝗲𝗿𝘀𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸 🌟 𝗧𝗼𝗽𝗼𝗹𝗼𝗴𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗮 𝗪𝗮𝘃𝗲𝗴𝘂𝗶𝗱𝗲 𝗕𝗲𝗻𝗱 Waveguide bends play a crucial role in directing light within photonic integrated circuits. However, compact bends often introduce losses that can hinder circuit performance. In a previous post, we explored shape optimization for waveguide bends. This time, we focus on topology optimization to achieve compact, low-loss waveguide bend designs. By optimizing a pixelated design region and applying a conic filter to smooth the permittivity at each iteration, followed by tanh projection for binarization, our approach converges quickly. In just 25 iterations (or 50 FDTD simulations), we generate a 3x3 micron, 90-degree waveguide bend with significantly reduced loss compared to traditional circular or Euler bends. 🔍 Want to see how it’s done? Check out this detailed example notebook: https://lnkd.in/gagArYdm. 💡Exciting news: inverse design is now available directly in the Tidy3D web GUI, making the process seamless and accessible, even for those without coding experience. Try out the waveguide bend example on the GUI: https://lnkd.in/gJiSZA2d! #InverseDesign #IntegratedPhotonics #Innovation #Tidy3D #Flexcompute

⭐ 𝗜𝗻𝘃𝗲𝗿𝘀𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸 ⭐ 𝗔𝗱𝗷𝗼𝗶𝗻𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗮 𝗪𝗮𝘃𝗲𝗹𝗲𝗻𝗴𝘁𝗵 𝗗𝗶𝘃𝗶𝘀𝗶𝗼𝗻 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲𝘅𝗲𝗿 Optical de-multiplexers are a crucial component in optical communications, but achieving compact and low-loss designs is challenging. Traditionally, engineers rely. on optical structures such as ring resonators or directional couplers, fine-tuning each component for specific wavelengths. This process is typically time-consuming and results in a large device footprint. In this context, inverse design offers an exciting opportunity for the efficient development of compact de-multiplexers in a single design step. This week, we explore topology optimization to simulate an efficient and compact optical de-multiplexer. Starting with an input waveguide and four output waveguides, we define an objective function for optimizing a 4 μm² design region, where each pixel is a free variable. Leveraging our API to handle simulation differentiation and fabrication constraints easily, we achieve a final design - a compact de-multiplexer optimized for telecommunications frequencies, with insertion losses close to zero! Dive into the design process in our interactive Python notebook here: https://lnkd.in/gmT_YGVZ #Photonics #InverseDesign #OpticalCommunications #OpticalEngineering #Tidy3D #Flexcompute

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⭐ 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗲 𝗧𝗶𝗱𝘆𝗚𝗿𝗮𝗱 - 𝘁𝗵𝗲 𝗲𝗮𝘀𝗶𝗲𝘀𝘁 𝘁𝗼 𝘂𝘀𝗲 𝗶𝗻𝘃𝗲𝗿𝘀𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝘁𝗼𝗼𝗹 𝗲𝘃𝗲𝗿 ⭐ TidyGrad is Tidy3D's inverse design tool that can solve large-scale, 3D inverse design problems in minutes to hours. TidyGrad uses automatic differentiation (AD) to simplify the inverse process as much as possible. The simulation code is integrated directly within common platforms for training machine learning models. Thereafter, one can write an objective function in regular Python code involving one or many Tidy3D simulations and arbitrary pre- and post-processing. TidyGrad's adjoint code is general, well-tested, and backed by massively parallel GPU solvers, making it extremely fast. The front-end code interfaces seamlessly with Python packages for machine learning, scientific computing, and visualization. Learn more about TidyGrad here: https://lnkd.in/gP3-Df8F Get a quick start from here: https://lnkd.in/gqJ9JM-V #InverseDesign #IntegratedPhotonics #Innovation #Tidy3D #Flexcompute

🌟 I𝗻𝘃𝗲𝗿𝘀𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸 🌟 𝗦𝗵𝗮𝗽𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗮 𝗖𝗼𝗺𝗽𝗮𝗰𝘁 𝗪𝗮𝘃𝗲𝗴𝘂𝗶𝗱𝗲 𝗕𝗲𝗻𝗱 Waveguide bends are one of the most fundamental components in integrated photonic circuits. Photonics designers often opt for larger bend radii to minimize loss, especially in low-contrast material platforms like silicon nitride or thin-film lithium niobate. However, increasing the bend radius conflicts with the need for compact circuit layouts. To achieve low-loss and compact waveguide bends, engineers traditionally turn to advanced shapes, such as Euler bends. But are there other, more efficient options? Inverse design presents a compelling alternative. This week, we explore how shape optimization can deliver a high-performance, compact waveguide bend. Starting with a conventional circular bend, we leverage inverse design shape optimization to minimize loss while respecting fabrication constraints by adding a curvature penalty term in the objective function. Within 30 iterations (or the equivalent of 60 FDTD simulations), the bent shape is optimized, resulting in a final design achieving approximately 95% transmission, a huge improvement over the circular bend with below 60% transmission. Curious about the optimization process? 🔎 Explore our interactive Python notebook, where we walk through the inverse design journey step-by-step: https://lnkd.in/gNASAG3q #InverseDesign #IntegratedPhotonics #Innovation #Tidy3D #Flexcompute

We're hosting Dynamic Stability Derivatives Using CFD. Make sure to attend it tomorrow: https://lnkd.in/dj9CqT2B

🌟 𝗜𝗻𝘃𝗲𝗿𝘀𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸 🌟 🔍 𝗦𝗵𝗮𝗽𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗮 𝗛𝗶𝗴𝗵-𝗡𝗔 𝗠𝗲𝘁𝗮𝗹𝗲𝗻𝘀 Metalens – a flat and ultra-thin lens made from metamaterials – represent an emerging optical technology. Designing a metalens traditionally involves mapping unit cell parameters to cover a 2π phase range and then arranging these cells to establish a phase profile that creates the desired focus position. This process can be time-consuming and presents challenges in addressing optical losses. The final design is often not optimal. 🤔 How great would it be to have an optimization tool that automatically handles everything? 💡 This week, we showcase how Tidy3D's inverse design capabilities make designing a high numerical aperture (NA) metalens as simple as defining the focus point! We start with a uniform distribution of Si rectangular prism unit cells, allowing the size of each unit cell to be a free parameter. Using the adjoint method, we can easily calculate the gradient of our objective function, which aims to maximize the intensity at the focal point while addressing fabrication constraints. After only 18 iterations (36 FDTD simulations), the size distributions of unit cells are arranged to achieve a metalens with a 0.94 NA operating at 840 nm! Check out our interactive Python notebook here: https://lnkd.in/eFiCJCCb #Photonics #Metalenses #InverseDesign #OpticalEngineering #Innovation #Tidy3D