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---
name: aallm-analog-circuit-design-llm
description: AaLLM for LLM analog circuit design.
trigger_words: analog circuit design, LLM circuit design, multi-agent circuit design, AaLLM
arxiv_id: 2608.13472
date_added: 2026-08-16
---
# AaLLM: End-to-End Analog Circuit Design Framework
## Overview
AaLLM is an open-source end-to-end multi-agent LLM workflow that automates analog circuit design from user specifications to complete netlist output, encompassing both topology generation and circuit sizing. This framework addresses the time-consuming, iterative nature of analog circuit design in nonlinear, high-dimensional design spaces.
## Core Methodology
### Key Components
1. **Automated Knowledge Base Creation**: Automatically extracts relevant technical knowledge from research papers and textbooks to combat manual data collection inefficiencies
2. **RAG-based Expert Emulation**: Implements Retrieval-Augmented Generation to emulate circuit design expertise using the automated knowledge base
3. **Tri-Agent Feedback System**:
- **Designer Agent**: Determines circuit component values
- **Critic Agent**: Scrutinizes the Designer's proposed values
- **Evaluator Agent**: Arbitrates between Designer and Critic to minimize sizing iterations
### Technical Innovations
- **End-to-End Integration**: Combines topology generation and circuit sizing in a single workflow (unlike fragmented conventional approaches)
- **Novel Topology Generation**: Capable of creating innovative circuit topologies beyond conventional designs
- **Efficiency Gains**: Achieves 3x-4.5x reduction in SPICE calls and 40x decrease in wall-clock time compared to existing approaches
- **Performance**: Generated novel topologies achieve comparable or up to 3x higher Figure of Merit (FoM) than known topologies
## Use Cases
- Automated analog circuit design from natural language specifications
- Rapid prototyping of novel circuit topologies
- Educational tool for circuit design exploration
- Integration into EDA (Electronic Design Automation) workflows
## Implementation Guidelines
1. **Input**: User specifications in natural language format
2. **Knowledge Base**: Automatically constructed from domain literature
3. **Multi-Agent Workflow**: Designer → Critic → Evaluator feedback loop
4. **Output**: Complete netlist with optimized component values
## Performance Metrics
- **Figure of Merit (FoM)**: Comparable to or up to 3x higher than conventional topologies
- **SPICE Calls**: 3x-4.5x reduction at inference time
- **Wall-clock Time**: 40x decrease compared to existing multi-agent LLM pipelines
## References
- arXiv:2608.13472 - "AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models"
- IEEE CDC 2026 (preliminary version accepted)
## Activation Keywords
analog circuit design, LLM circuit design, multi-agent circuit design, AaLLM, automated circuit synthesis, topology generation, circuit sizing