mlsanityData to 14 Oct 2026

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

arXiv 2608.13472 ↗First posted 13 Aug 2026eess.SY cs.AI cs.SYcited by 0

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Abstract (arXiv)

Analog circuit design is a time-consuming, iterative process in a nonlinear and high-dimensional design space that relies heavily on expert intuition. Among recent developments, LLMs have introduced a promising approach by bringing natural language reasoning to circuit design tasks. The majority of conventional LLM-based approaches provide fragmented solutions that focus either only on sizing or topology generation. These methods require adding specific technical knowledge manually, which is inefficient and prone to hallucinations during circuit sizing. Moreover, the inherent trade-off in meeting different specs makes current approaches iterative and tedious. Another shortcoming is the inability to create innovative topologies, which may lead to sub-optimal designs due to reliance on conventional topologies. In this paper, we present AaLLM, an open-source end-to-end multi-agent LLM workflow that takes user specs as input and outputs the appropriate netlist, encompassing both topology generation and circuit sizing. AaLLM automates the creation of a relevant knowledge base from research papers and textbooks to combat tedious manual data collection. A RAG model is implemented to emulate circuit design expertise using this knowledge base. Moreover, AaLLM uses a novel tri-agent feedback system comprising a Designer that determines circuit component values, a Critic that scrutinizes these values, and an Evaluator that minimizes circuit sizing iterations by arbitrating between the other two agents. AaLLM-generated novel topologies achieve a figure of merit (FoM) comparable to that of known topologies, and up to 3x higher for certain circuits. Testing on several circuit topologies, our results show a 3x - 4.5x decrease in the number of SPICE calls at inference when compared to SOTA multi-agent LLM pipelines. The results also show a 40x decrease in wall-clock time compared to existing approaches.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 13 Aug 2026.

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22 of 34 references.

References · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2024AnalogCoder: Analog Circuit Design via Training-Free Code Generationcited by 84
  5. 2023CktGNN: Circuit Graph Neural Network for Electronic Design Automationcited by 58
  6. 2020AutoCkt: Deep Reinforcement Learning of Analog Circuit Designscited by 51
  7. 2025AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologiescited by 37
  8. 2024LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuitscited by 33
  9. 2024AmpAgent: An LLM-based Multi-Agent System for Multi-stage Amplifier Schematic Design from Literature for Process and Performance Portingcited by 29
  10. 2024LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuitscited by 22
  11. 2024AnalogXpert: Automating Analog Topology Synthesis by Incorporating Circuit Design Expertise into Large Language Modelscited by 18
  12. 2024AMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAGcited by 16
10 more
  1. 2025AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMscited by 15
  2. 2025AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generationcited by 11
  3. 2025AnaFlow: Agentic LLM-based Workflow for Reasoning-Driven Explainable and Sample-Efficient Analog Circuit Sizingcited by 11
  4. 2025MuaLLM: A Multimodal Large Language Model Agent for Circuit Design Assistance with Hybrid Contextual Retrieval-Augmented Generationcited by 8
  5. 2025MenTeR: A fully-automated Multi-agenT workflow for end-to-end RF/Analog Circuits Netlist Designcited by 7
  6. 2025EasySize: Elastic Analog Circuit Sizing via LLM-Guided Heuristic Searchcited by 5
  7. 2026AutoSizer: Automatic Sizing of Analog and Mixed-Signal Circuits via Large Language Model (LLM) Agentscited by 5
  8. 2026SINA: A Circuit Schematic Image-to-Netlist Generator Using Artificial Intelligencecited by 4
  9. 2025Accelerating OTA Circuit Design: Transistor Sizing Based on a Transformer Model and Precomputed Lookup Tablescited by 1
  10. 2026ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Explorationcited by 1

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  2. 2025A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction
  3. 2026AgenticSizing: A Large Language Model-based Multi-Agent Framework for Analog Circuit Sizing
  4. 2025CIRCUIT: A Benchmark for Circuit Interpretation and Reasoning Capabilities of LLMs
  5. 2026AnalogAgent: Self-Improving Analog Circuit Design Automation with LLM Agents
  6. 2025TopoSizing: An LLM-aided Framework of Topology-based Understanding and Sizing for AMS Circuits
  7. 2025EEschematic: Multimodal-LLM Based AI Agent for Schematic Generation of Analog Circuit
  8. 2025AnalogSAGE: Self-evolving Analog Design Multi-Agents with Stratified Memory and Grounded Experience