TMM-Fast: A Transfer Matrix Computation Package for Multilayer Thin-Film Optimization

Achieving the desired optical response from a multilayer thin-film structure over a broad range of wavelengths and angles of incidence can be challenging. An advanced thin-film structure can consist of multiple materials with different thicknesses and numerous layers. Design and optimization of complex thin-film structures with multiple variables is a computationally heavy problem that is still under active research. To enable fast and easy experimentation with new optimization techniques, we propose the Python package TMM-Fast which enables parallelized computation of reflection and transmission of light at different angles of incidence and wavelengths through the multilayer thin-film. By decreasing computational time, generating datasets for machine learning becomes feasible and evolutionary optimization can be used effectively. Additionally, the sub-package TMM-Torch allows to directly compute analytical gradients for local optimization by using PyTorch Autograd functionality. Finally, an OpenAi Gym environment is presented which allows the user to train reinforcement learning agents on the problem of finding multilayer thin-film configurations.Comment: Technical note, 8 pages, introduction to Python package TMM-Fast, Repository: https://github.com/MLResearchAtOSRAM/tmm_fast

Parameterized Reinforcement Learning for Optical System Optimization

Designing a multi-layer optical system with designated optical characteristics is an inverse design problem in which the resulting design is determined by several discrete and continuous parameters. In particular, we consider three design parameters to describe a multi-layer stack: Each layer's dielectric material and thickness as well as the total number of layers. Such a combination of both, discrete and continuous parameters is a challenging optimization problem that often requires a computationally expensive search for an optimal system design. Hence, most methods merely determine the optimal thicknesses of the system's layers. To incorporate layer material and the total number of layers as well, we propose a method that considers the stacking of consecutive layers as parameterized actions in a Markov decision process. We propose an exponentially transformed reward signal that eases policy optimization and adapt a recent variant of Q-learning for inverse design optimization. We demonstrate that our method outperforms human experts and a naive reinforcement learning algorithm concerning the achieved optical characteristics. Moreover, the learned Q-values contain information about the optical properties of multi-layer optical systems, thereby allowing physical interpretation or what-if analysis.Comment: Presented as a poster at the workshop on machine learning for engineering modeling, simulation and design @ NeurIPS 202

Directional emission of white light via selective amplification of photon recycling and Bayesian optimization of multi-layer thin films

Over the last decades, light-emitting diodes (LED) have replaced common light bulbs in almost every application, from flashlights in smartphones to automotive headlights. Illuminating nightly streets requires LEDs to emit a light spectrum that is perceived as pure white by the human eye. The power associated with such a white light spectrum is not only distributed over the contributing wavelengths but also over the angles of vision. For many applications, the usable light rays are required to exit the LED in forward direction, namely under small angles to the perpendicular. In this work, we demonstrate that a specifically designed multi-layer thin film on top of a white LED increases the power of pure white light emitted in forward direction. Therefore, the deduced multi-objective optimization problem is reformulated via a real-valued physics-guided objective function that represents the hierarchical structure of our engineering problem. Variants of Bayesian optimization are employed to maximize this non-deterministic objective function based on ray tracing simulations. Eventually, the investigation of optical properties of suitable multi-layer thin films allowed to identify the mechanism behind the increased directionality of white light: angle and wavelength selective filtering causes the multi-layer thin film to play ping pong with rays of light

Fully convolutional networks for void segmentation in X-ray images of solder joints

Whether in sensory or illumination applications, optoelectronic components are an essential part of our everyday life. To actually include them in smartphones, cars or other products, they need to be soldered onto the surface of a printed circuit board via surface-mounted technology. Hereby, the solder joint is formed by remelting the solder paste that was printed onto the board before the optical component was mounted. During this process, the evaporating flux causes porosities (voids) filled with gas that is caught in the solidifying alloy. Voids influence the thermal and electric properties of a solder joint and hence reduce its reliability. Because the solder joint is embedded between the device and the board, non-destructive X-ray inspection is used to visualize voids. However, the superposition of various structures in a noisy image acquisition process renders the semantic segmentation of solder joints and voids in X-ray images difficult. To the best of our knowledge, there is no method for automatic void segmentation in flat solder joints based on X-ray images aside from this work. We develop a fully convolutional network for pixel-wise classification of X-ray images and show, how our contributions enable automatic void inspection of soldered structures without protracted X-ray tomography of flat samples, so-called laminography