New AI methods optimize analog circuit design with improved efficiency and reliability · 3 sources tracked

COVERAGE [3]

1. arXiv cs.LG TIER_1 English(EN) · Mustafa Emre G\"ursoy, Stefan Uhlich, Ryoga Matsuo, Ya\u{g}{\i}z Gen\c{c}er, Arun Venkitaraman, Chia-Yu Hsieh, Andrea Bonetti, Eisaku Ohbuchi, Lorenzo Servadei · 2026-07-16 04:00

   Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points

   arXiv:2607.14008v1 Announce Type: new Abstract: In this paper, we introduce Lighthouse RL, a sample-efficient reinforcement learning (RL) approach for analog circuit sizing. Traditional methods lack generalization across different performance targets, while standard RL approaches…

2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Lorenzo Servadei · 2026-07-15 16:57

   SPECS: Speciated Evolutionary Circuit Synthesis

   We propose SPECS, a genetic algorithm for automated analog circuit synthesis with joint topology and sizing optimization. SPECS is inspired by NeuroEvolution of Augmenting Topologies (NEAT), an evolutionary algorithm originally developed to synthesize neural networks. By reformul…

3. arXiv cs.LG TIER_1 English(EN) · Lorenzo Servadei · 2026-07-15 16:37

   Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points

   In this paper, we introduce Lighthouse RL, a sample-efficient reinforcement learning (RL) approach for analog circuit sizing. Traditional methods lack generalization across different performance targets, while standard RL approaches waste resources exploring unpromising regions. …