mlsanityData to 14 Oct 2026

GLOVA: Global and Local Variation-Aware Analog Circuit Design with Risk-Sensitive Reinforcement Learning

arXiv 2505.11208 ↗First posted 16 May 2025cs.AI cs.CE cs.ETcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Analog/mixed-signal circuit design encounters significant challenges due to performance degradation from process, voltage, and temperature (PVT) variations. To achieve commercial-grade reliability, iterative manual design revisions and extensive statistical simulations are required. While several studies have aimed to automate variation aware analog design to reduce time-to-market, the substantial mismatches in real-world wafers have not been thoroughly addressed. In this paper, we present GLOVA, an analog circuit sizing framework that effectively manages the impact of diverse random mismatches to improve robustness against PVT variations. In the proposed approach, risk-sensitive reinforcement learning is leveraged to account for the reliability bound affected by PVT variations, and ensemble-based critic is introduced to achieve sample-efficient learning. For design verification, we also propose μ-σ evaluation and simulation reordering method to reduce simulation costs of identifying failed designs. GLOVA supports verification through industrial-level PVT variation evaluation methods, including corner simulation as well as global and local Monte Carlo (MC) simulations. Compared to previous state-of-the-art variation-aware analog sizing frameworks, GLOVA achieves up to 80.5× improvement in sample efficiency and 76.0× reduction in time.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 27 references that are in the map. First posted on arXiv 16 May 2025.

Builds on

6 of 27 references.

References · 6

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 374
  3. 2020GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learningcited by 89
  4. 2021An Efficient Batch Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multi-objective Acquisition Ensemblecited by 13
  5. 2020Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Explorationcited by 5
  6. 2022RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RLcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Formally Verifying Analog Neural Networks Under Process Variations Using Polynomial Zonotopescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration
  2. 2024INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers
  3. 2026Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points
  4. 2026ARCS: Autoregressive Circuit Synthesis with Topology-Aware Graph Attention and Spec Conditioning
  5. 2026Simulation-Aware In-Context Policy Improvement for LLM-Aided Analog Layout Refinement
  6. 2025AUTOCIRCUIT-RL: Reinforcement Learning-Driven LLM for Automated Circuit Topology Generation
  7. 2026Can an Actor-Critic Optimization Framework Improve Analog Design?
  8. 2025Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning