Deep learning-enabled framework for automatic lens design starting point generation

We present a simple, highly modular deep neural network (DNN) framework to address the problem of automatically inferring lens design starting points tailored to the desired specifications. In contrast to previous work, our model can handle various and complex lens structures suitable for real-world problems such as Cooke Triplets or Double Gauss lenses. Our successfully trained dynamic model can infer lens designs with realistic glass materials whose optical performance compares favorably to reference designs from the literature on 80 different lens structures. Using our trained model as a backbone, we make available to the community a web application that outputs a selection of varied, high-quality starting points directly from the desired specifications, which we believe will complement any lens designer’s toolbox

Lens Tasarımcısının Optik Lens Geliştirme Süreçleri

Teknolojinin gelişmesi ile bilgi işlem gücü artmış, bilimsel hesaplama yapma yeteneği de hızla gelişmiştir. Gelişen teknolojinin etkilediği alanlardan biri de optik alanıdır. Lens tasarımı, içerisinde lens optimizasyonu, ışın izi analizi, lens çizimi, modülasyon transfer fonksiyon hesaplamaları gibi birçok alt yapıyı içinde barındıran karmaşık bir çalışma alanıdır. Optik lens tasarımı, maliyet ve üretim sınırlamaları dahil olmak üzere bir dizi performans gereksinimini ve kısıtlamasını karşılayacak bir lens tasarlama sürecidir. Bu lens tasarım süresi, merceğin içinden geçen ışığın nasıl etkilendiğini modellemek için ışın izleme tekniğinin veya diğer tekniklerin kullanarak hesaplanması açısından oldukça karmaşıktır. Ancak optik ile ilgili yapılan çalışmalar incelendiğinde, birçok çalışmanın optik sistemler hakkında ayrıntılı bilgi verdikleri ancak lens tasarımı ve prosedürleri hakkında yeterli açıklama yapmadıkları belirlenmiştir. Bu çalışmada okuyucunun ışın izleme prosedürlerine, eksen dışı verilere ve üçüncü dereceden sapmalara aşina olduğu varsayılmaktadır. Ayrıca okuyucunun lens tasarımı ve analiz yapılmasına yönelik bir bilgisayar yazılımına erişimi olduğu varsayılmaktadır. Bu çalışma kapsamında, bir lensin tasarım sürecinin en başından, (son ürün olan) lensin çizimine kadar ki süreç ayrıntılı bir şekilde açıklanmaya çalışılmıştır. Araştırmanın literatüre katkı sağlayacağı düşünülmektedir

Analysis of frequency stability and thermoelastic effects for slotted tuning fork MEMS resonators

MicroElectroMechanical Systems (MEMS) resonators are attracting increasing interest because of their smaller size and better integrability as opposed to their quartz counterparts. However, thermal drift of the natural frequency of silicon structures is one of the main issues that has hindered the development of MEMS resonators. Extensive investigations have addressed both the fabrication process (e.g., introducing heavy doping of the silicon) and the mechanical design (e.g., exploiting proper orientation of the device, slots, nonlinearities). In this work, starting from experimental data published in the literature, we show that a careful design can help reduce the thermal drift even when slots are inserted in the devices in order to decrease thermoelastic losses. A custom numerical code able to predict the dynamic behavior of MEMS resonators for different materials, orientations and doping levels is coupled with an evolutionary optimization algorithm and the possibility to find an optimal mechanical design is demonstrated on a tuning-fork resonator

Analysis of frequency stability and thermoelastic effects for slotted tuning fork MEMS resonators

MicroElectroMechanical Systems (MEMS) resonators are attracting increasing interest because of their smaller size and better integrability as opposed to their quartz counterparts. However, thermal drift of the natural frequency of silicon structures is one of the main issues that has hindered the development of MEMS resonators. Extensive investigations have addressed both the fabrication process (e.g., introducing heavy doping of the silicon) and the mechanical design (e.g., exploiting proper orientation of the device, slots, nonlinearities). In this work, starting from experimental data published in the literature, we show that a careful design can help reduce the thermal drift even when slots are inserted in the devices in order to decrease thermoelastic losses. A custom numerical code able to predict the dynamic behavior of MEMS resonators for different materials, orientations and doping levels is coupled with an evolutionary optimization algorithm and the possibility to find an optimal mechanical design is demonstrated on a tuning-fork resonator

Génération de designs de lentilles avec l'apprentissage profond

Concevoir une lentille, que ce soit pour l'astronomie, la microscopie ou la vision numérique, est un problème de taille visant à trouver un compromis idéal entre la qualité d'image et les différentes contraintes. Par une procédure d'essais-erreurs, une approche typique consiste à sélectionner un point de départ parmi une banque de designs optiques puis à l'optimiser dans l'espoir de satisfaire les présents requis. Cette approche n'exploite pas pleinement la montagne d'information contenue dans les banques de designs : un seul de ces designs contribue au problème à la fois, et seulement s'il répond approximativement aux spécifications et à la configuration désirée. Comment peut-on faire mieux ? L'hypothèse de départ de cette thèse est que l'on peut utiliser l'apprentissage automatique pour extraire et exploiter les caractéristiques communes aux designs de haute qualité que l'on retrouve dans ces banques de données. Concrètement, ces designs conçus par des experts contribuent à l'entraînement d'un modèle d'apprentissage profond qui prend en entrée les spécifications désirées et retourne tous les paramètres nécessaires pour modéliser une lentille. Le contenu de cette thèse, qui détaille le développement de ce cadre d'extrapolation de lentilles, peut se résumer en trois principales contributions. Premièrement, nous définissons et validons un objectif d'entraînement qui compense pour la rareté des données disponibles, soit en intégrant le problème d'optimisation de lentilles directement à la boucle d'entraînement du modèle. Deuxièmement, nous élaborons un modèle dynamique qui acquiert une représentation commune pour toutes les lentilles indépendamment de leur configuration, ce qui nous permet d'extrapoler la banque de designs pour générer des lentilles sur de nouvelles configurations. Troisièmement, nous ajustons le cadre pour refléter le caractère multimodal de la conception afin d'inférer plusieurs lentilles de structures différentes pour n'importe quel ensemble de spécifications et de configuration de lentille. Avec une portée adéquate et un entraînement réussi, ce cadre d'extrapolation de lentilles représente un outil inédit pour la conception optique : une fois le modèle déployé, il permet d'obtenir sur demande des points de départ de haute qualité, variés et sur mesure, et ce, en un temps minimal.Designing a lens, whether for astronomy, microscopy, or computer vision, is a challenging task that seeks an ideal balance between image quality and various constraints. Through a trial-and-error process, a typical approach consists in selecting a starting point in a lens design database and optimizing it to hopefully satisfy the problem at hand. This approach, however, does not fully harness the wealth of information contained in lens design databases: only one such design contributes to the problem at a time, and only if it approximately meets the desired specifications and configuration. How can we do better? The premise of this work is that machine learning can be used to extract and exploit the common features of the high-quality designs contained in lens design databases. Specifically, the expertly conceived designs that compose these databases are used to guide the training process of a deep learning-based model, which receives the design specifications as input and returns all the parameters needed to fully represent a lens. The content of the thesis, which details the development of this lens design extrapolation framework, can be summarized in three main contributions. First, we define and validate a training objective that compensates for the scarcity of available data, by integrating the lens optimization problem directly into the model training loop. Second, we develop a dynamic model that acquires a common representation for all lenses regardless of their configuration, allowing us to extrapolate the lens database to generate lenses on new, unseen configurations. Third, we extend the framework to capture the multimodal nature of lens design, so that multiple lenses with different structures can be inferred for any given set of specifications and configuration. With a suitable scope and a successful training process, this lens design extrapolation framework offers a new and valuable tool for lens designers: once the model is deployed, only a minimal amount of time is required to obtain varied, high-quality starting points that are tailored to the desired specifications

User-preference based evolutionary algorithms for many-objective optimisation

Evolutionary Algorithms (EA) have enjoyed great success in finding solutions for multi-objective problems that have two or three-objectives in the past decade. The majority of these Evolutionary Multi-objective Optimisation (EMO) algorithms explored the decision-space using the selection pressure governed methods that are based on dominance relation. Although these algorithms are effective locating solutions for multi-objective problems, they have not been very successful for problem instances having more than three objectives, usually named as many-objective problems. The main reason behind this shortcoming is the fact that the dominance comparison becomes ineffective as the number of objectives increases. In this thesis, we incorporate some user-preference methods into EMO algorithms to enhance their ability to handle many-objective problems. To this end, we introduce a distance metric derived from user-preference schemes such as the reference point method and light beam search found in multi-criteria decision making. This distance metric is used to guide the EMO algorithm to locate solutions within certain areas of the objective-space known as preferred regions. In our distance metric approach, the decision maker is allowed to specify the amount of spread of solutions along the solution front as well. We name this distance metric based EMO algorithm as d-EMO, which is a generalised framework that can be constructed using any EA. This distance metric approach is computationally less expensive as it does not rely on dominance ranking methods, but very effective in solving many-objective problems. One key issue that remains to be resolved is that there are no suitable metrics for comparing the performance of these user-preference EMO algorithms. Therefore, we introduce a variation of the normalised Hyper-Volume (HV) metric suitable for comparing user-preference EMO algorithms. The key feature in our HV calculation process is to consider only the solutions within each preferred region. This methodology favours user-preference EMO algorithms that have converged closely to the Pareto front within a preferred region. We have identified two real-world engineering design problems in optimising aerofoil and lens designs, and formulated them as many-objective problems. The optimisation process of these many-objective problems is computationally expensive. Hence, we use a reference point PSO algorithm named MDEPSO to locate solutions effectively in fewer function evaluations. This PSO algorithm is less prone to getting stuck in local optimal fronts and still retains its fast convergence ability. In MDEPSO, this feature is achieved by generating leader particles using a differential evolution rule rather than picking particles directly from the population or an external archive. The main feature of the optimisation process of these aerofoil and lens design problems is the derivation of reference points based on existing designs. We illustrate how these existing designs can be used to either obtain better or new design solutions that correspond to various requirements. This process of deriving reference points based on existing design models, and integrating them into a user-preference EMO framework is a novel approach in the optimisation process of such computationally expensive engineering design problems