RFChipAgent is a novel multi‑agent flow that uses large language model agents to automate end‑to‑end analog/RF circuit design under human supervision. The system comprises four key components: a retrieval‑augmented generation subsystem that pulls design knowledge from documentation, agents that handle topology selection and schematic/testbench assembly, a hybrid sizing engine that blends TPE and CMA‑ES optimization with simulator feedback, and a trust‑scored simulation database that informs future optimization runs. Validation on GF22FDSOI 60 GHz wideband mm‑wave low‑noise amplifiers demonstrates automated topology generation, specification‑driven exploration, and simulator‑guided optimization, yielding significant reductions in design effort while preserving sign‑off quality.
By Awani Khodkumbhe, Yunfei Feng, Raj Rangarajan, Kevin Wang, Kamal Sahota
arXiv:2606. 01862v1 Announce Type: cross Abstract: Translating user intents into physical radio signals represents the critical yet notoriously tedious final step in wireless prototyping, as it requires intricate knowledge of physical layer details and presents immense implementation challenges.
By Jiazhen Lei, Tianze Cao, Yuxin Sha, Sihan Wang, Bingbing Wang, Fengyuan Zhu, Zeming Yang, Xiaohua Tian
Analog circuit sizing remains a challenging and time-consuming task due to the large design space, strong performance trade-offs, and increasing circuit complexity in scaled technologies. Although rec...
VortexChat is an agentic framework that autonomously performs end-to-end inverse design of integrated photonic devices from natural language specifications. It combines a large language model decision agent with topology generation, gradient-based refinement, and full-wave electromagnetic simulation in a closed-loop architecture, enabling iterative decomposition of design objectives and minimal human intervention. The system successfully generated devices meeting the Vortex100 Benchmark metrics and fabricated a broadband terahertz perfect vortex beam multiplexer that matched simulation predictions.
By Faqian Chong, Yulun Wu, Shilong Li, Andrew Forbes, Hongsheng Chen, Song Han
K-TRAIL is a simulator-guided generative framework that uses diffusion-based layout generation combined with derivative-free ensemble Kalman guidance to design RF and electromagnetic circuits. It allows a black-box EM simulator to refine candidate layouts during generation, supporting synthesis from target S-parameter responses or direct RF performance constraints. Experiments on multi-layer RFIC structures demonstrate improved agreement with target responses and the ability to discover structurally distinct layouts that meet design requirements.
By Piyush Saha, Evan Newell, Hanna O'Leary, Arun Natarajan, Alireza Aghasi
AbsorbEvo is an agentic framework that autonomously designs microwave absorbers by translating natural‑language performance goals into full‑wave‑simulated designs. It combines language reasoning, physics‑based prediction, and historical feedback to guide a candidate evolution strategy, using a low‑cost predictive model to rank designs before simulation. In tests on AbsorbBench‑36, AbsorbEvo achieved a 79.17% task success rate, outperforming generic agents and random search.
By Zhicheng Feng, Yubo Zhao, Xuefeng Yao