Prof. Taiyun Chi

RICE University(米RICE大学)のTaiyun Chi准教授にご講演いただきました。

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Speaker: Prof. Taiyun Chi (Rice University)

Title: Toward AI-Assisted Analog and RF IC Design Automation

Abstract: LLMs have advanced rapidly over the past three years, with frontier systems now supporting trillion-scale parameter counts, million-token context windows, multimodal inputs/outputs, stronger reasoning, coding, computer use, and agentic workflows. At the same time, the way we work with LLMs has shifted accordingly, from prompt engineering to context engineering, and more recently to harness engineering. These advances and paradigm shifts have enabled many great successes in “AI for X,” especially in coding. This talk will present our group’s recent progress toward integrating LLMs into analog and RF IC design, a field that has conventionally remained manual, time-consuming, and dependent on deep domain expertise. We will discuss what today’s AI systems can do, where they still fail, and which directions appear most promising for analog and RF IC design automation. I will also cover AI techniques beyond LLMs that enable fast design-space exploration and performance optimization.

Bio: Taiyun Chi leads the Rice Integrated Systems and Electromagnetics (RISE) Lab, focusing on analog/RF/mmWave circuits and micro-systems design for wireless communication and neuro-engineering applications. Since joining Rice in 2019, his group has received multiple Best Paper awards and nominations, including the IEEE International Solid-State Circuits Conference (ISSCC) Lewis Winner Outstanding Paper Award in 2025 and 2024, the IEEE International Microwave Symposium (IMS) Advanced Practice Paper Award Finalist in 2024 and 2021, the IEEE RFIC Symposium Industry Paper Award Finalist in 2023, the IEEE RFIC Symposium Best Student Paper Award Finalist in 2022, and the IEEE Custom Integrated Circuits Conference (CICC) Best Student Paper Award in 2021. He was a recipient of the NSF