Deep Neural Network–Based Detection and Verification of Microelectronic Images

Reza, Alimoor; Chen, Zhenhua; Crandall, David J.

doi:10.1007/s41635-019-00088-4

Cited by 18 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This creates a plasma that emits light whose spectrum can be characterized using a spectrometer. LIBS can target almost all elements of the periodic table; thus, it has been used extensively to characterize electronic waste [109][110][111][112][113], including Ta, Zr, and Ba in capacitors [114,115], Au and Ag in ICs [110] and rare-earth elements in specific components (speakers, Hard Disk Drive magnets) [116]. We note that the literature is not limited to ECs on WPCBs.…”

Section: Sorting Using Spectroscopymentioning

confidence: 99%

Dismantling of Printed Circuit Boards Enabling Electronic Components Sorting and Their Subsequent Treatment Open Improved Elemental Sustainability Opportunities

et al. 2021

View full text Add to dashboard Cite

This critical review focuses on advanced recycling strategies to enable or increase recovery of chemical elements present in waste printed circuit boards (WPCBs). Conventional recycling involves manual removal of high value electronic components (ECs), followed by raw crushing of WPCBs, to recover main elements (by weight or value). All other elements remain unrecovered and end up highly diluted in post-processing wastes or ashes. To retrieve these elements, it is necessary to enrich the waste streams, which requires a change of paradigm in WPCB treatment: the disassembly of WPCBs combined with the sorting of ECs. This allows ECs to be separated by composition and to drastically increase chemical element concentration, thus making their recovery economically viable. In this report, we critically review state-of-the-art processes that dismantle and sort ECs, including some unpublished foresight from our laboratory work, which could be implemented in a recycling plant. We then identify research, business opportunities and associated advanced retrieval methods for those elements that can therefore be recovered, such as refractory metals (Ta, Nb, W, Mo), gallium, or lanthanides, or those, such as the platinum group elements, that can be recovered in a more environmentally friendly way than pyrometallurgy. The recovery methods can be directly tuned and adapted to the corresponding stream.

show abstract

Section: Sorting Using Spectroscopymentioning

confidence: 99%

Dismantling of Printed Circuit Boards Enabling Electronic Components Sorting and Their Subsequent Treatment Open Improved Elemental Sustainability Opportunities

et al. 2021

View full text Add to dashboard Cite

show abstract

“…A common application of vision-based detection of electronic components is inspecting the integrity and quality of PCBs [ 13 , 14 , 15 ]. Image classification techniques based on deep neural networks have been used to detect integrated circuit (IC) components and verify their correct placement on the finished PCB product in [ 13 ]. Verification is similar to classification and a best accuracy of 92.31% was achieved.…”

Section: Introductionmentioning

confidence: 99%

“…A potential deep learning model for detecting and classifying parts is Faster R-CNN [ 20 ]. However, the drawback of using Faster R-CNN for classifying electronic parts is explained in [ 13 ]. It achieves very poor results and according to the authors Faster R-CNN is not designed for small, relatively featureless objects such as ICs.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Detection and Classification of Used Electronic Parts

Chand

Lal

2022

Sensors

View full text Add to dashboard Cite

Economic and environmental sustainability is becoming increasingly important in today’s world. Electronic waste (e-waste) is on the rise and options to reuse parts should be explored. Hence, this paper presents the development of vision-based methods for the detection and classification of used electronics parts. In particular, the problem of classifying commonly used and relatively expensive electronic project parts such as capacitors, potentiometers, and voltage regulator ICs is investigated. A multiple object workspace scenario with an overhead camera is investigated. A customized object detection algorithm determines regions of interest and extracts data for classification. Three classification methods are explored: (a) shallow neural networks (SNNs), (b) support vector machines (SVMs), and (c) deep learning with convolutional neural networks (CNNs). All three methods utilize 30 × 30-pixel grayscale image inputs. Shallow neural networks achieved the lowest overall accuracy of 85.6%. The SVM implementation produced its best results using a cubic kernel and principal component analysis (PCA) with 20 features. An overall accuracy of 95.2% was achieved with this setting. The deep learning CNN model has three convolution layers, two pooling layers, one fully connected layer, softmax, and a classification layer. The convolution layer filter size was set to four and adjusting the number of filters produced little variation in accuracy. An overall accuracy of 98.1% was achieved with the CNN model.

show abstract

“…As the controlled objects become increasingly complex, it is usually impossible to obtain an accurate system model. 1–3…”

Section: Introductionmentioning

confidence: 99%

“…As the controlled objects become increasingly complex, it is usually impossible to obtain an accurate system model. [1][2][3] In recent years, many different neural network control methods have been proposed for a variety of unknown nonlinear systems. [4][5][6][7][8][9] These methods are implemented by an adaptive NN control strategy to compensate the uncertainties of the system and the external environment.…”

Section: Introductionmentioning

confidence: 99%

Disturbance observer-based control with adaptive neural network for unknown nonlinear system

Zheng

2022

Measurement and Control

View full text Add to dashboard Cite

This paper presents an adaptive neural network controller based on a disturbance observer to compensate the disturbance caused by neural network approximation for a class of unknown nonlinear systems. The proposed adaptive neural network control with an updated parameters mechanism is not subject to the restriction of compact set assumption for satisfying the universal approximation property. The neural network approximation error can be compensated online through the proposed disturbance observer. The proposed method eliminates the need to obtain an exact system model before applying the ideal controller. The effectiveness of the proposed method is validated by numerical simulations.

show abstract

Deep Neural Network–Based Detection and Verification of Microelectronic Images

Cited by 18 publications

References 16 publications

Dismantling of Printed Circuit Boards Enabling Electronic Components Sorting and Their Subsequent Treatment Open Improved Elemental Sustainability Opportunities

Dismantling of Printed Circuit Boards Enabling Electronic Components Sorting and Their Subsequent Treatment Open Improved Elemental Sustainability Opportunities

Vision-Based Detection and Classification of Used Electronic Parts

Disturbance observer-based control with adaptive neural network for unknown nonlinear system

Contact Info

Product

Resources

About