Design of a Microwave Lowpass – Bandpass Filter using Deep Learning and Artificial Intelligence

Roshani, Saeed; Heshmati, Hossein; Roshani, Sobhan

doi:10.33969/jiec.2021.31001

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Artificial neural network technology has been widely applied in the modeling and design of microwave components or circuits, including the modeling of on-chip square spiral inductors [13], the development of efficient and accurate models of CPW-related components [14], and the modeling of small-signal and large-signal models of field-effect transistors combined with equivalent circuit modeling and ANN modeling technology [15]. It also involves research directions such as the optimization of filter design [16][17][18], power amplifiers [19], and microstrip antennas [20]. Another design idea based on ANN modeling is called "neural network inverse modeling" [21], from which there have been some research results [22][23][24].…”

Section: Modeling and Applications Of Artificial Neural Networkmentioning

confidence: 99%

Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization

Ye,

Wu,

Chen

et al. 2023

Applied Sciences

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Microstrip filters are widely used in high-frequency circuit design for signal frequency selection. However, designing these filters often requires extensive trial and error to achieve the desired performance metrics, leading to significant time costs. In this work, we propose an automated design flow for hairpin filters, a specific type of microstrip filter. We employ artificial neural network (ANN) modeling techniques to predict the circuit performance of hairpin filters, and leverage the efficiency of low-cost models to deploy reinforcement learning agents. Specifically, we use the proximal policy optimization (PPO) reinforcement learning algorithm to learn abstract design actions for the filters, allowing us to achieve automated optimization design. Through simulation results, we demonstrate the effectiveness of the proposed approach. By optimizing the geometric dimensions, we significantly improve the performance metrics of hairpin filters, and the trained agent successfully meets our specified design goals within 5 to 15 design steps. This work serves as a conceptual validation attempt to apply reinforcement learning techniques and pre-trained ANN models to automate MMIC filter design. It exhibits clear advantages in terms of time-saving and performance efficiency when compared to other optimization algorithms.

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Section: Modeling and Applications Of Artificial Neural Networkmentioning

confidence: 99%

Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization

Ye,

Wu,

Chen

et al. 2023

Applied Sciences

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Leveraging AI in Photonics and Beyond

et al. 2022

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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.

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AI-Powered Antennas and Microwave Components

Talpur,

Muneer,

Memon

et al. 2023

Advances in Linguistics and Communication Studies

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In wireless communication systems, high-performance antenna, microwave, and radio frequency design systems are essential to meet end-user requirements. As demand for these components increases, it's crucial to design optimized structures in a short amount of time with guaranteed best results. This has led to the need for a higher level of intelligence in the design process. Artificial intelligence (AI) techniques such as evolutionary algorithms (EAs), machine learning (ML), deep learning (DL), and knowledge representation have been widely used to find parameter values of antenna and microwave components, leading to optimized designs in minimum processing time and overcoming long processing times and poor results. This chapter focuses on the major AI methods in the area of antenna, microwave, and other radio frequency (RF) components, including phase shifters, intelligent reflective surfaces (RIS), waveguides, filters, stubs, etc. The chapter discusses different EAs and ML algorithms and their use in optimizing antenna and microwave designs.

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Design of a Microwave Lowpass – Bandpass Filter using Deep Learning and Artificial Intelligence

Cited by 3 publications

References 16 publications

Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization

Intelligent Design of Hairpin Filters Based on Artificial Neural Network and Proximal Policy Optimization

Leveraging AI in Photonics and Beyond

AI-Powered Antennas and Microwave Components

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