Design Automation of Analog and Mixed-Signal Circuits Using Neural Networks–A Tutorial Brief
G Liñán-Cembrano, N Lourenço… - … on Circuits and …, 2023 - ieeexplore.ieee.org
This tutorial brief shows how Artificial Neural Networks (ANNs) can be used for the
optimization and automated design of analog and mixed-signal circuits. A survey of …
optimization and automated design of analog and mixed-signal circuits. A survey of …
Machine learning in advanced IC design: A methodological survey
The increasing complexity and size of design space poses significant challenges for
integrated circuit (IC) design. This article discusses the potential of machine learning (ML) …
integrated circuit (IC) design. This article discusses the potential of machine learning (ML) …
Design and optimization of low-dropout voltage regulator using relational graph neural network and reinforcement learning in open-source SKY130 Process
Z Li, AC Carusone - 2023 IEEE/ACM International Conference …, 2023 - ieeexplore.ieee.org
Design automation and optimization for analog integrated circuits (ICs) are challenging,
especially for transistor sizing. Given certain design specifications and circuit topology …
especially for transistor sizing. Given certain design specifications and circuit topology …
Rose-opt: Robust and efficient analog circuit parameter optimization with knowledge-infused reinforcement learning
Design automation of analog circuits has long been sought. However, achieving robust and
efficient analog design automation remains challenging. This paper proposes a learning …
efficient analog design automation remains challenging. This paper proposes a learning …
Performance-driven wire sizing for analog integrated circuits
Analog IC performance has a strong dependence on interconnect RC parasitics, which are
significantly affected by wire sizes in recent technologies, where minimum-width wires have …
significantly affected by wire sizes in recent technologies, where minimum-width wires have …
Post-layout simulation driven analog circuit sizing
Post-layout simulation provides accurate guidance for analog circuit design, but post-layout
performance is hard to be directly optimized at early design stages. Prior work on analog …
performance is hard to be directly optimized at early design stages. Prior work on analog …
Deep reinforcement learning for analog circuit sizing with an electrical design space and sparse rewards
Y Uhlmann, M Essich, L Bramlage, J Scheible… - Proceedings of the …, 2022 - dl.acm.org
There is still a great reliance on human expert knowledge during the analog integrated
circuit sizing design phase due to its complexity and scale, with the result that there is a very …
circuit sizing design phase due to its complexity and scale, with the result that there is a very …
RoSE: Robust Analog Circuit Parameter Optimization with Sampling-Efficient Reinforcement Learning
Design automation of analog circuits has been a long-standing challenge in the integrated
circuit field. Recently, multiple methods based on learning or optimization have …
circuit field. Recently, multiple methods based on learning or optimization have …
An Open-Source AMS Circuit Optimization Framework Based on Reinforcement Learning-From Specifications to Layouts
Z Li, AC Carusone - IEEE Access, 2024 - ieeexplore.ieee.org
This paper presents a fully open-sourced AMS integrated circuit optimization framework
based on reinforcement learning (RL). Specifically, given a certain circuit topology and …
based on reinforcement learning (RL). Specifically, given a certain circuit topology and …
Reinforcing the Connection between Analog Design and EDA
Building upon recent advances in analog electronic design automation (EDA), this paper
discusses directions for reinforcing the connection between design and EDA, in order to …
discusses directions for reinforcing the connection between design and EDA, in order to …