北洋微电讲堂||第36期预告

张齐军，博士，教授，博士生导师，加拿大工程院院士、加拿大工程研究院院士、 IEEE Fellow。1982年毕业于南京理工大学，获工学学士学位;1987年毕业于加拿大麦克马斯特大学，获电气工程博士学位。1990年，加入加拿大卡尔顿大学电子系，现任Chancellor’s Professor。

出版《Neural Networks for RF and Microwave Design》 、《Modeling and Simulation of High-Speed VLSI Interconnects》和《Simulation-Driven Design Optimization and Modeling for Microwave Engineering》。在高速/高频电子设计的建模、优化和机器学习领域发表了360多篇文章。曾于1999年和2002年两次担任《International Journal of RF/Microwave Computer-Aided Engineering》“Special Issues on Applications of ANN for RF/Microwave Design”特刊的客座编辑，并于2021年担任《IEEE Microwave Magazine》“Machine Learning in Microwave Engineering”特刊的客座共同编辑，现担任《IEEE Transactions on Microwave Theory and Techniques》副主编，《IEEE Microwave Magazine 》主题编辑，IEEE微波理论与技术(MTT)协会未来方向委员会微波人工智能和机器学习技术工作组联合主席。

AI and machine learning techiniques are important techniques for microwave computer-aided design to perform forward/inverse modeling for active/passive components to enhance microwave circuit design. With measured or simulated data of microwave circuits, AI models, e.g., artificial neural networks (ANNs), can be trained to learn relevant microwave relationships which are otherwise computationally expensive or for which efficient analytical formulas are not available. By training an ANN using data from electromagnetic (EM)/physics simulations, one can use the trained ANN as models for microwave circuits to replace the EM/physics models, which are typically CPU-intensive, to significantly accelerate circuit design with EM/physics-level accuracies. This talk describes various recent advances of AI and machine learning techniques for microwaves.