arXiv AI August 14, 2026 By Mohammed Ayman Habib, Rylan Hart, Morteza Fayazi

AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models

arXiv:2608. 13472v1 Announce Type: cross Abstract: Analog circuit design is a time-consuming, iterative process in a nonlinear and high-dimensional design space that relies heavily on expert intuition.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 17

Towards Reliable AI-Assisted Analog Design: Template-Constrained LLM Agents for SAR ADC Generation

arXiv:2607. 14165v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have demonstrated significant capability in software code generation, their application to analog Electronic Design Automation (EDA) is bottlenecked.

By Dimple Vijay Kochar, Hae-Seung Lee, Anantha P. Chandrakasan

llms agents multimodal safety

arXiv Machine Learning

Jul 3

A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA

arXiv:2504. 03711v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI)-driven electronic design automation (EDA) techniques have been extensively explored for VLSI circuit design applications.

By Wenji Fang, Jing Wang, Yao Lu, Shang Liu, Yuchao Wu, Yuzhe Ma, Zhiyao Xie

llms fine-tuning

arXiv AI

2d ago

Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement

arXiv:2608. 13767v1 Announce Type: new Abstract: Analog IC layout design remains a labor-intensive iterative process dominated by simulation-driven refinement.

By Bingyang Liu, Ziming Wei, Xiaohan Gao, David Z. Pan

llms agents

arXiv AI

Aug 6

ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration

arXiv:2608. 04999v1 Announce Type: cross Abstract: Analog circuit design automation using reinforcement learning (RL) has emerged as a promising approach for reducing manual effort.

By Osei Brempong, Mohammed Ayman Habib, Vivan Poddar, Morteza Fayazi

llms reinforcement-learning benchmarks

arXiv AI

5d ago

SynAct: A Reasoning-Acting Large Language Model Agent for Adaptive Synthesis Optimization

arXiv:2608. 12751v1 Announce Type: cross Abstract: Logic synthesis transforms RTL designs into gate-level netlists, where PPA results are highly sensitive to the choice of optimization commands, making synthesis tuning both high-dimensional and expensive.

By Fangzhou Liu, Peiyi Han, Jiawei Liu, Yuan Pu, Zhuolun He, Rongliang Fu, Tsung-Yi Ho, Bei Yu

llms agents safety

arXiv Machine Learning

Jun 25

ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments

arXiv:2606. 25207v1 Announce Type: new Abstract: Hyperparameter Optimization (HPO) is essential for maximizing machine learning model performance, and its core challenge is sample efficiency: finding strong configurations within a limited budget.

By Taicheng Guo, Haomin Zhuang, Kehan Guo, Yujun Zhou, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

llms agents safety

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Towards Reliable AI-Assisted Analog Design: Template-Constrained LLM Agents for SAR ADC Generation

A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDA

Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement

ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration

SynAct: A Reasoning-Acting Large Language Model Agent for Adaptive Synthesis Optimization

ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments