Virtual Simulation of Integrated Circuits Combining AP with DE Algorithm

As computers develops, virtual simulation technology becomes an important means of integrated circuit design. Therefore, based on the demand for virtual simulation of integrated circuits, a simulation method combining affinity propagation and differential evolution algorithm was proposed. By applying the affinity propagation to circuit fault diagnosis and combining it with differential evolution algorithm, circuit parameters optimization was carried out. These experiments confirm that the fusion of affinity propagation and differential evolution algorithm has a precision of 94.26%, recall of 93.41%, mean F1 of 88.59%, convergence speed of 56.77 seconds, and stability of 93.17%. The affinity propagation performs well in clustering. Especially without pre-defining the classes, it can identify the position and number of class centers automatically. The simulation of integrating affinity propagation and differential evolution algorithm has broad application prospects in virtual simulation of integrated circuits. It can improve simulation effectiveness and performance, providing effective support for circuit design and testing.

Authors

Publication Details

Journal
Electronics ETF
Published
2025-07-16
DOI
https://doi.org/10.53314/els2529043h
Citations
1
Primary Topic
VLSI and FPGA Design Techniques
Type
article
Field-Weighted Citation Impact
0.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Virtual Simulation of Integrated Circuits Combining AP with DE Algorithm

Peipei Hu
1 citations
Electronics ETF
VLSI and FPGA Design Techniques
0.73
article

Virtual Simulation of Integrated Circuits Combining AP with DE Algorithm

Peipei Hu
article en
1 citations

Abstract

As computers develops, virtual simulation technology becomes an important means of integrated circuit design. Therefore, based on the demand for virtual simulation of integrated circuits, a simulation method combining affinity propagation and differential evolution algorithm was proposed. By applying the affinity propagation to circuit fault diagnosis and combining it with differential evolution algorithm, circuit parameters optimization was carried out. These experiments confirm that the fusion of affinity propagation and differential evolution algorithm has a precision of 94.26%, recall of 93.41%, mean F1 of 88.59%, convergence speed of 56.77 seconds, and stability of 93.17%. The affinity propagation performs well in clustering. Especially without pre-defining the classes, it can identify the position and number of class centers automatically. The simulation of integrating affinity propagation and differential evolution algorithm has broad application prospects in virtual simulation of integrated circuits. It can improve simulation effectiveness and performance, providing effective support for circuit design and testing.

Electronics ETFVol. 29(2)
Openalex Percentile: Top 26%
VLSI and FPGA Design Techniques
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.