Circuit-centric Genetic Algorithm (CGA) for Analog and Radio-Frequency Circuit Optimization
This paper presents an automated method for optimizing parameters in analog/high-frequency circuits, aiming to maximize performance parameters of a radio-frequency (RF) receiver. The design target includes a reduction of power consumption and noise figure and an increase in conversion gain. This study investigates the use of an artificial algorithm for the optimization of a receiver, illustrating how to fulfill the performance parameters with diverse circuit parameters. To overcome issues observed in the traditional Genetic Algorithm (GA), the concept of the Circuit-centric Genetic Algorithm (CGA) is proposed as a viable approach. The new method adopts an inference process that is simpler and computationally more efficient than the existing deep learning models. In addition, CGA offers significant advantages over manual design of finding optimal points and the conventional GA, mitigating the designer's workload while searching for superior optimum points.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design
Analog and radio-frequency circuit design requires extensive exploration of both circuit topology and parameters to meet specific design criteria like power consumption and bandwidth. Designers must review state-of-the-a…
SpiceMixer -- Netlist-Level Circuit Evolution
This paper introduces SpiceMixer, a genetic algorithm developed to synthesize novel analog circuits by evolving SPICE netlists. Unlike conventional methods, SpiceMixer operates directly on netlist lines, enabling compati…
Attack of the Genes: Finding Keys and Parameters of Locked Analog ICs Using Genetic Algorithm
Hardware intellectual property (IP) theft is a major issue in today's globalized supply chain. To address it, numerous logic locking and obfuscation techniques have been proposed. While locking initially focused on digit…
Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization
The design automation of analog circuits is a longstanding challenge. This paper presents a reinforcement learning method enhanced by graph learning to automate the analog circuit parameter optimization at the pre-layout…
Graph LearningAutomated Placement of Analog Integrated Circuits using Priority-based Constructive Heuristic
This paper presents a heuristic approach for solving the placement of Analog and Mixed-Signal Integrated Circuits. Placement is a crucial step in the physical design of integrated circuits. During this step, designers ch…