Optimization of Passive Chip Components Placement with Self-Alignment Effect for Advanced Surface Mounting Technology

Parviziomran, Irandokht; Cao, Shun; Yang, Hyunseok; Park, Seungbae; Won, Daehan

doi:10.1016/j.promfg.2020.01.313

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…In general, the SMT process contains three operations: the stencil printing process (SPP), pick-and-place (chip mounting) process, and reflow soldering [1][2][3]. First, solder paste is printed onto the surface of a PCB by a stencil printer.…”

Section: Introductionmentioning

confidence: 99%

The Dissimilar Self-Alignment Characteristics of Smaller Passive Components in the Length and Width Directions

Cen

et al. 2021

Journal of SMT

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Motivation: As passive components’ size gets smaller, quality rejects due to overhang and misalignment after the reflow appear more frequently. This situation is partly because the pass-fail criterion is set based on the offset concerning the component dimensions. Therefore, understanding the self-alignment characteristics of electronic components becomes very critical for surface-mount assembly yield. This research investigates the dissimilarity of self-alignment in the length and width directions. Approach: To avoid the argument of sample to sample variations, data are collected from 81 printed circuit boards (PCB) and 182,250 assembled components. Within a PCB, 25 different solder paste printing offset locations and 81 component placement offset settings are implemented. Component-placement positions before and after the reflow are monitored. The results are compared to identify different component sizes’ self-alignment characteristics in the length and width directions. Key findings: The misalignment of smaller passive components, e.g., R0402M(0.40 mm × 0.20 mm), is worse than the larger component under the identical solder paste printing and component placement conditions. Furthermore, the self-alignment characteristic in the length direction of these passive components, e.g., R0402M, to R1005M (1.00 mm × 0.50 mm) is superior to that of width direction. The observations are not consistent with the results found in earlier research that reported on larger components, e.g., C0402M(0.40 mm × 0.20 mm), to C3216M(3.20 mm × 1.50 mm).

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Section: Introductionmentioning

confidence: 99%

The Dissimilar Self-Alignment Characteristics of Smaller Passive Components in the Length and Width Directions

Cen

et al. 2021

Journal of SMT

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“…Hidden Markov models are also widely used in ECG segmentation because they are powerful tools for considering the temporal dependency among the waveforms [13][14][15]. The majority of the studies on machine learning-based methods have utilized sparse signal processing to represent an approximation of the nonlinear ECG signal using sparsity constraints [16][17][18][19][20][21]. Some studies have also applied deep learning techniques to detect the ECG waveforms considering its high performance in various classification tasks [22,23].…”

Section: Introductionmentioning

confidence: 99%

A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection

Fotoohinasab,

Hocking,

Afghah

2021

Preprint

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The electrocardiogram (ECG) signal is the most widely used non-invasive tool for the investigation of cardiovascular diseases. Automatic delineation of ECG fiducial points, in particular the R-peak, serves as the basis for ECG processing and analysis. This study proposes a new method of ECG signal analysis by introducing a new class of graphical models based on optimal changepoint detection models, named the graph-constrained changepoint detection (GCCD) model. The GCCD model treats fiducial points delineation in the non-stationary ECG signal as a changepoint detection problem. The proposed model exploits the sparsity of changepoints to detect abrupt changes within the ECG signal; thereby, the R-peak detection task can be relaxed from any preprocessing step. In this novel approach, prior biological knowledge about the expected sequence of changes is incorporated into the model using the constraint graph, which can be defined manually or automatically. First, we define the constraint graph manually; then, we present a graph learning algorithm that can search for an optimal graph in a greedy scheme. Finally, we compare the manually defined graphs and learned graphs in terms of graph structure and detection accuracy. We evaluate the performance of the algorithm using the MIT-BIH Arrhythmia Database. The proposed model achieves an overall sensitivity of 99.64%, positive predictivity of 99.71%, and detection error rate of 0.19 for the manually defined constraint graph and overall sensitivity of 99.76%, positive predictivity of 99.68%, and detection error rate of 0.55 for the automatic learning constraint graph.

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“…It was concluded that placing components on the pastes had better performances than placing them on the pad. Recently, non-linear programming models were employed in the P&P process to identify the optimal placement locations based on the predicted components movement information [10].…”

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confidence: 99%

A novel placement method for mini-scale passive components in surface mount technology

Cen

et al. 2021

Int J Adv Manuf Technol

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This paper aims to propose a novel placing method, i.e., place-between-paste-andpad (PB), for mini-scale passive components to enhance electronic assembly lines' yield. PB means a component is designed to be placed at the midpoint between the pastes and pads on the length direction while it aligns with the pads' center on the width direction. An experiment that involves 12 printed circuit boards (PCB) is designed and conducted to get comparative results. Four PCBs are employed for place-on-pad (PP), place-on-paste (PPS), and PB separately. On each board, 375 resistors R0402M (0.40 mm × 0.20 mm) are assembled horizontally.To study the components' misalignment under various solder paste offset conditions in different placement methods, a stencil with 25 solder paste offset settings is utilized. Based on this experiment's results, PB has superior performance to the other two methods to minimize components' misalignment. Regarding the number of acceptable components when post-reflow offsets are within 25% of components' dimensions, PB and PP have equivalent performances, and they both outperform PPS. Furthermore, PB is a lowcost placing strategy because PB needs not the real-time communication between the solder paste inspection machine and the pickand-place machine. With the miniaturization trend in electronic products, the postreflow components' misalignment is more frequently observed than before. The placement method proposed in this study is expected to offer a low-cost exploration in the component pick-and-place procedure to enhance the surface mount technology (SMT) assembly quality.Keywords SMT assembly • mini-scale passive components • pick-and-place • place-on-pad • place-on-paste beneficial for large components in decreasing 56 the misalignment [3]-[5], it did not perform so 57 well for mini-scale components.

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Optimization of Passive Chip Components Placement with Self-Alignment Effect for Advanced Surface Mounting Technology

Cited by 7 publications

References 10 publications

The Dissimilar Self-Alignment Characteristics of Smaller Passive Components in the Length and Width Directions

The Dissimilar Self-Alignment Characteristics of Smaller Passive Components in the Length and Width Directions

A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection

A novel placement method for mini-scale passive components in surface mount technology

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