Advanced Topics in Modelling Microwave and mmWave Electron Devices
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".
Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 7024
Special Issue Editors
Interests: computer-aided design; electron devices; microwave electronics; multiphysics modeling
Interests: RF high power amplifier (HPA) design; automated circuit design; optimization algorithms applied to HPA designs using machine learning; antenna designs; analog circuit and system designs (CMOS)
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Special Issue Information
Dear Colleagues,
RF and Microwave electronics represents a key research field that can enable a wide variety of applications, including wireless telecommunications, radars and satellite surveillance systems, space commercialization and, more recently, sensing systems for quantum electronics. The continuous development of semiconductor technologies dedicated to microwave and mm wave applications, both in compound semiconductors (GaAs, GaN, InP) and silicon, poses ever-increasing challenges to the device modelling community. The availability of accurate device models and their efficient implementation in physical and circuit analysis is the key enabling factor allowing for the deployment of microwave integrated circuits with reduced cost, high reliability and fast time to market. Despite this, modeling electron devices in microwave systems is especially challenging. Device models must take into account multiple features in terms of nonlinearity, memory, reliability, noise and thermal management.
Multi-physics analysis, including electrical, thermal and electromagnetic analysis, has always represented a typical feature of microwave modelling, especially when including passive structures. New trends in the modelling community investigate time domain, frequency domain and mixed approaches to target specific modelling needs, e.g., to mimic the response to broadband amplitude modulated signals, the long-term memory effects due to trap dynamics in GaN HEMTs or the nonlinear stability analysis. Sophisticated characterization techniques have also been specifically developed to assist the model identification process. A very promising field is also represented by artificial intelligence, which will prospectively revolutionize the design of microwave circuits in terms of timesaving and optimization capability.
The continuous scaling of device dimensions, as well as the massive exploitation of silicon-based devices, have caused the inclusion of advanced concepts for electronic transport and quantization to take a decisive step further. Microwave systems for quantum detection and sensing also foster nanotechnologies to become prime actors of future microwave systems and require specifically developed models in terms of noise, transport and quantum description, especially in the cryogenic regime. Furthermore, technological reliability becomes poorer in emerging devices and the impact of variability on analog performance is harsher than in digital applications.
This Special Issue is devoted to collecting selected papers, both original research papers and reviews, on the peculiar modelling approaches required for microwave and mm wave electron devices, joining the modelling community efforts towards setting the pathway to address the challenges of future technologies and applications.
Relevant topics include, but are not limited to:
- Models for RF, microwave and mm-wave technologies: GaN HEMTs, metamorphic HEMTs, FinFETs, nanodevices;
- Physics-based models; compact models; behavioral models;
- ANN modeling of microwave components;
- Multiphysics simulation: electromagnetic, thermal, traps, electronic transport, quantum confinement;
- Time domain/frequency domain/envelope domain models;
- Stability analysis;
- Noise models;
- Cryogenic models;
- Sensitivity, statistical and reliability analysis.
Prof. Dr. Simona Donati Guerrieri
Dr. Lida Kouhalvandi
Guest Editors
Manuscript Submission Information
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Keywords
- nonlinear device models
- ANN modelling
- multiphysics modelling
- HEMTs models
- cryogenic models
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