Frontiers in Educational Research, 2025, 8(10); doi: 10.25236/FER.2025.081007.
Benqing Guo1, Jing Gong2
1Microelectronics School, Chengdu University of Information Technology, Chengdu, China
2West China Hospital, Sichuan University, Chengdu, China
This paper reports a teaching reform for the micro major course “Millimeter Wave Amplifier Design” within the “Advanced Chip Design and Innovation” track. Confronting limited contact hours and the cross-disciplinary nature of mmWave IC design, we integrate an RFIC-GPT tool to automate the transformer/inductor synthesis, shortening passive layout iteration and lowering entry barriers. A representative 30 GHz transformer-coupled, noise-canceling LNA serves as the anchor case to link theoretical trade-offs with a design workflow (specification input, schematic design, AI-assisted passive layout generation, EM extraction, final co-simulation). Past semesters of practice show ~25% average reduction in passive design time and sustained student engagement. Results evidence AI augmented exemplars effectively enhancing IC talent cultivation.
Millimeter-Wave Education, Low-Noise Amplifier, AI-Assisted EDA, Passive Synthesis, Noise Cancellation, Practical Teaching Reform, Micro-Major Curriculum
Benqing Guo, Jing Gong. AI-Enabled Teaching Reform of a Millimeter-Wave Amplifier Design Course: Accelerating Passive Synthesis with RFIC-GPT for a 30 GHz Noise-Cancelling LNA Case Study. Frontiers in Educational Research (2025), Vol. 8, Issue 10: 44-52. https://doi.org/10.25236/FER.2025.081007.
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