Efficient Iterative Process Based on an Improved Genetic Algorithm for Decoupling Capacitor Placement at Board Level

Paulis, Francesco de; Cecchetti, Riccardo; Olivieri, Carlo; Piersanti, Stefano; Orlandi, Antonio; Buecker, Markus

doi:10.3390/electronics8111219

Cited by 7 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While less explored than mainstream approaches like Faster R-CNN or YOLO, autoencoder-based object detection methods offer the advantage of learning robust and concise feature representations from input data. De Paulis et al [30] proposed an advanced PCB inspection system utilizing a skipconnected convolutional autoencoder to identify defect shapes and locations. Makwana et al [31] also introduced PCBSegClassNet, an encoder-decoder architecture designed to segment and classify PCB components.…”

Section: B Other Neural Network-based Methodsmentioning

confidence: 99%

Enhancing EfficientNet-YOLOv4 for Integrated Circuit Detection on Printed Circuit Board (PCB)

Chi,

Wahab,

Mohamed

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Ensuring printed circuit board (PCB) quality and functionality during manufacturing demands precise automated visual inspection. Detecting integrated circuits (ICs) on PCBs poses a significant challenge due to diverse component sizes, types, and intricate board markings, complicating accurate object detection. This study addresses this challenge by proposing an enhanced EfficientNet-YOLOv4 algorithm tailored explicitly for IC detection on PCBs. Integrating EfficientNet's robust feature extraction capabilities with YOLOv4's precise object localization, the methodology employs diverse data augmentation techniques to enrich the training dataset. Extensive experiments showcase the algorithm's efficacy and robustness in handling complex PCB layouts and varying lighting conditions, outperforming existing PCB inspection models. The proposed method, EfficientNetv2-L-YOLOv4, achieves an impressive F1-score of 99.22 with an inference speed of 0.135 seconds. The proposed method also performed well compared to EfficientNet-B7-FasterRCNN and original YOLOv4; it attains an F1-score of 98.96 and an inference speed of 0.102 seconds (with a batch size of 4). These results underscore the significance of effective feature extraction networks in object detection. Beyond addressing IC detection challenges, this algorithm advances computer vision and object detection fields. Implementing EfficientNetv2-L-YOLOv4 in real manufacturing scenarios holds promise for automating component inspection, potentially eliminating human intervention. INDEX TERMSAutomated visual inspection, Feature extraction networks, Object detection, Printed circuit board (PCB).

show abstract

Section: B Other Neural Network-based Methodsmentioning

confidence: 99%

Enhancing EfficientNet-YOLOv4 for Integrated Circuit Detection on Printed Circuit Board (PCB)

Chi,

Wahab,

Mohamed

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The proposed model used the evolutionary multi-objective NSGA-II algorithm to adjust the random-forest model to estimate the resistance and inductive reactance accurately. In addition, Paulis et al [ 17 ] employed genetic algorithms to optimize decoupling capacitors for PDN design at the PCB level to obtain a frequency spectrum at various locations. A close relationship between the measured results and the simulated input impedance revealed the effectiveness of the proposed method when validated on the board.…”

Section: Introductionmentioning

confidence: 99%

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Lai

Lin

et al. 2023

Micromachines

View full text Add to dashboard Cite

The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedance are closely related to the PCB production process, circuit designers and manufacturers must work together to adjust the target impedance to maintain signal integrity. Five machine learning models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and light gradient boosting machine (LightGBM), were used to forecast target impedance values. Furthermore, the Optuna algorithm is used to determine forecasting model hyperparameters. This study applied tree-based machine learning techniques with Optuna to predict impedance. The results revealed that five tree-based machine learning models with Optuna can generate satisfying forecasting accuracy in terms of three measurements, including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2). Meanwhile, the LightGBM model with Optuna outperformed the other models. In addition, by using Optuna to tune the parameters of machine learning models, the accuracy of impedance matching can be increased. Thus, the results of this study suggest that the tree-based machine learning techniques with Optuna are a viable and promising alternative for predicting impedance values for circuit analysis.

show abstract

Digital Economic Dispatch System Based on Improved Genetic Algorithm

Liu

2023

Lecture Notes on Data Engineering and Communications Technologies

View full text Add to dashboard Cite

Efficient Iterative Process Based on an Improved Genetic Algorithm for Decoupling Capacitor Placement at Board Level

Cited by 7 publications

References 34 publications

Enhancing EfficientNet-YOLOv4 for Integrated Circuit Detection on Printed Circuit Board (PCB)

Enhancing EfficientNet-YOLOv4 for Integrated Circuit Detection on Printed Circuit Board (PCB)

Tree-Based Machine Learning Models with Optuna in Predicting Impedance Values for Circuit Analysis

Digital Economic Dispatch System Based on Improved Genetic Algorithm

Contact Info

Product

Resources

About