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Abstract
Artificial intelligence (AI) techniques have been spreading in most scientific areas and have become a heated focus in photonics research in recent years. Forward modeling and inverse design using AI can achieve high efficiency and accuracy for photonics components. With AI-assisted electronic circuit design for photonics components, more advanced photonics applications have emerged. Photonics benefit a great deal from AI, and AI, in turn, benefits from photonics by carrying out AI algorithms, such as complicated deep neural networks using photonics components that use photons rather than electrons. Beyond the photonics domain, other related research areas or topics governed by Maxwell’s equations share remarkable similarities in using the help of AI. The studies in computational electromagnetics, the design of microwave devices, as well as their various applications greatly benefit from AI. This article reviews leveraging AI in photonics modeling, simulation, and inverse design; leveraging photonics computing for implementing AI algorithms; and leveraging AI beyond photonics topics, such as microwaves and quantum-related topics.
Highlights
Electromagnetics is a fundamental branch of physics that arises from the interaction of charged particles with ever-expanding technological applications and scientific discoveries [1,2,3,4,5]
We recently proposed the use of a N × N star coupler [124,125], as shown in Figure 7, which is a diffractive component, to perform the Fourier transform [113] and the convolution operation, to be used in convolutional neural networks (CNNs) [126]
As we have discussed in this review article, photonics benefits a great deal from Artificial intelligence (AI) for efficient soft computing and inverse design
Summary
Electromagnetics is a fundamental branch of physics that arises from the interaction of charged particles with ever-expanding technological applications and scientific discoveries [1,2,3,4,5]. AI methods, such as machine learning (ML) techniques are proven methodologies for the capture, interpolation, and optimization of highly complex phenomena in many fields They are widely used in image classification [23,24], image/video processing [17,25,26], natural language processing (NLP) [27,28], and robotics [29,30]. Coupling AI techniques with traditional physics-based methods could potentially discover pseudo-random designs with performance excellence that is beyond physical intuitions This entire cycle of design, modeling, and simulation carried by soft computing algorithms will accelerate execution speeds by two to three orders of magnitude. Photonics covers a wide range of the electromagnetic spectrum from visible to mid-infrared wavelengths This encapsulates vast applications that include data transport, telecommunication, quantum information technologies, biology, and chemical sensing.
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