IEEE AP/ED/MTT North Italy Chapter

Abstract: This talk focusses on system and circuit design considerations for highly-integrated radar transceivers in CMOS and SiGe HBT technologies. The speaker will first provide motivation for realization of radar sensors at mm-wave frequencies by showing the possible applications. Then, frequency band allocations for radar at mm-wave frequencies are discussed. Next, speaker will discuss system level consideration in detail and will present step-by-step system design steps for an integrated fast-chirp FMCW radar transceiver, such as level budget calculation, phase noise considerations, PLL linearity, design of the analog baseband. The system considerations will be systematically translated into specifications of circuit blocks (e.g. LNA, mixer, PA, VCO, analog baseband etc.) of the radar transceiver. Additionally, digital modulation techniques such as phase-modulated continuous-wave (PMCW) will be discussed and a systematic comparison with FMCW will be given. Next, technology-dependent considerations and challenges related to critical building blocks are discussed (e.g. phase noise, noise figure, operating frequency, routing density, digital baseband). Then, the speaker will present several design examples of integrated radar transceivers operating at V-band and D-band and will discuss the circuit architectures. The talk is rounded out by a vision on novel modulation techniques and trends in MIMO radar array realizations. Speaker(s): Prof. Vadim Issakov Room: Nyquist Meeting room, Bldg: Cittadella Politecnica, IV floor, corso Duca degli Abruzzi 24, Torino, Piemonte, Italy

Recent research in deep learning techniques has attracted much attention. They have also been applied to electromagnetic engineering. Data-driven approaches allow machines to “learn” from a large amount of data and “master” the physical laws in certain controlled boundary conditions. However, this technique also faces many challenges, such as inaccuracy, limited generalization ability, etc. In electromagnetic engineering, physical laws, i.e., Maxwell’s equations, set major guidelines in research and development. They discover the nature of electromagnetic fields and waves and are universal across various scenarios. Incorporating physical principles into the deep learning framework significantly improves deep neural networks' learning capacity and generalization ability, hence increasing the accuracy and reliability of deep learning techniques in modeling electromagnetic phenomena. In this talk, we will study several techniques to embed physical simulation into deep learning to model electromagnetic wave propagation. With the help of both physical simulation and deep learning, we can improve the accuracy and computational efficiency of electromagnetic modeling and data inversion. Hybridizing fundamental physical principles with “knowledge” from big data could help electromagnetic technologies be more automatic, accurate, and reliable. Speaker(s): Prof. Maokun LI Room: 2R, Bldg: DICAM, Via Mesiano 77, Trento, Trentino-Alto Adige, Italy, Virtual: https://events.vtools.ieee.org/m/479465

This talk explores the transformative role of metasurfaces as versatile platforms for next-generation electromagnetic (EM) wave manipulation across a wide range of applications. Metasurfaces have the potential to revolutionize communication, sensing, imaging, and sustainable energy by enabling unprecedented control over EM waves in ultra-thin and highly customizable structures. The presentation will cover advanced metasurface applications, such as reconfigurable intelligent surfaces for 5G/6G communication, high-sensitivity sensors for medical and environmental monitoring, high-resolution imaging systems, and energy-efficient absorbers for solar and thermophotovoltaic devices. By examining recent breakthroughs and future directions, this talk aims to inspire innovative solutions and collaborations within the AP-S community. Speaker(s): Prof. Muhammad Zubair, Virtual: https://events.vtools.ieee.org/m/473785