ANALYSIS OF FAILURE FACTORS IN RUNNING LED CIRCUIT ASSEMBLY USING BREADBOARD AND PCB AMONG VOCATIONAL ELECTRONICS ENGINEERING STUDENTS
Abstract
Running LED circuit assembly is a widely implemented introductory practicum module in vocational electronics engineering education, yet students consistently encounter operational failures during assembly activities using both breadboard and PCB media. This study aimed to analyze the dominant failure factors in running LED circuit assembly and compare operational failure tendencies between breadboard and PCB media among vocational electronics engineering students. A quantitative descriptive survey was conducted using a five-point Likert-scale questionnaire distributed to 50 students at Sriwijaya State Polytechnic. Results indicated that in breadboard assembly, damaged components and IC overheating was the dominant failure factor (M = 3.54), followed by incorrect polarity installation (M = 3.30) and incorrect wiring (M = 3.24). In PCB assembly, broken copper traces and open circuit defects dominated (M = 3.62), followed by solder bridge defects and incorrect component placement (M = 3.50). Comparative analysis revealed that PCB assembly demonstrated a higher overall operational failure tendency than breadboard assembly, with mean scores of 3.10–3.18 versus 2.86–2.92 respectively. These findings suggest that breadboard failures are predominantly reversible and rooted in power mishandling, while PCB failures are largely permanent and driven by fabrication process execution. Practicum instruction should prioritize power supply verification for breadboard and etching process control for PCB assembly.
Downloads
References
A. Alessandrini, “A study of students engaged in electronic circuit wiring in an undergraduate course,” J. Sci. Educ. Technol., vol. 32, no. 1, pp. 78–95, Feb. 2023, doi: 10.1007/s10956-022-09994-9.
D. R. Dounas-Frazer and H. J. Lewandowski, “Electronics lab instructors’ approaches to troubleshooting instruction,” Phys. Rev. Phys. Educ. Res., vol. 13, no. 1, p. 010102, 2017, doi: 10.1103/PhysRevPhysEducRes.13.010102.
K. A. Peppler, R. M. Sedas, and N. Thompson, “Paper circuits vs. breadboards: Materializing learners’ powerful ideas around circuitry and layout design,” J. Sci. Educ. Technol., vol. 32, no. 4, pp. 469–492, 2023, doi: 10.1007/s10956-023-10029-0.
Texas Instruments, NE555 Precision Timers Datasheet. Dallas, TX, USA: Texas Instruments, 2023. [Online]. Available: https://www.ti.com/lit/ds/symlink/ne555.pdf
Texas Instruments, CD4017B CMOS Decade Counter/Divider with 10 Decoded Outputs Datasheet. Dallas, TX, USA: Texas Instruments, 2023. [Online]. Available: https://www.ti.com/lit/ds/symlink/cd4017b.pdf
W. Zhong and T. Walther, “Failure analysis of some commercial spotlights based on light emitting diodes,” Electronics, vol. 11, no. 1, p. 48, 2022, doi: 10.3390/electronics11010048.
H. Yao, Y. Li, Y. Zhang et al., “Failure mechanism and analysis diagnosis of LED,” MAPAN, vol. 37, no. 1, pp. 195–206, 2022, doi: 10.1007/s12647-021-00518-1.
Z. Chen, Q. Zhang, K. Wang, X. Luo, and S. Liu, “Reliability test and failure analysis of high power LED packages,” J. Semicond., vol. 32, no. 1, p. 014007, 2011, doi: 10.1088/1674-4926/32/1/014007.
K. L. Van De Bogart, D. R. Dounas-Frazer, H. J. Lewandowski, and M. R. Stetzer, “Investigating the role of socially mediated metacognition during collaborative troubleshooting of electric circuits,” Phys. Rev. Phys. Educ. Res., vol. 13, no. 2, p. 020116, 2017, doi: 10.1103/PhysRevPhysEducRes.13.020116.
H. B. Park, S. K. Hong, and R. Tomasek, “Reducing PCB solder process defects and improving PCB solder joint reliability through computer simulation,” in Proc. 12th Annu. IEEE Semicond. Thermal Meas. Manage. Symp., Austin, TX, USA, Mar. 1996, doi: 10.1109/STHERM.1996.545101.
R. M. Atta, “Effect of applying air pressure during wet etching of micro copper PCB tracks with ferric chloride,” Int. J. Mater. Res., vol. 113, no. 9, pp. 795–808, 2022, doi: 10.1515/ijmr-2021-8343.
Q. Ling and N. A. M. Isa, “Printed circuit board defect detection methods based on image processing, machine learning and deep learning: A survey,” IEEE Access, vol. 11, pp. 15921–15944, 2023, doi: 10.1109/ACCESS.2023.3245093.
X. Chen, Y. Wu, X. He, and W. Ming, “A comprehensive review of deep learning-based PCB defect detection,” IEEE Access, vol. 11, pp. 139017–139038, 2023, doi: 10.1109/ACCESS.2023.3339561.
K. P. Anoop, N. S. Sarath, and V. V. Sasi Kumar, “A review of PCB defect detection using image processing,” Int. J. Eng. Innovative Technol., vol. 4, no. 11, pp. 188–192, 2015. [Online]. Available: https://www.scribd.com/document/746911971/journal-pcb
A. Bhattacharya and S. G. Cloutier, “End-to-end deep learning framework for printed circuit board manufacturing defect classification,” Sci. Rep., vol. 12, p. 12559, Jul. 2022, doi: 10.1038/s41598-022-16302-3.
A. Bianchi, K. J. Moon, A. Dementyev, and S. Je, “BlinkBoard: Guiding and monitoring circuit assembly for synchronous and remote physical computing education,” HardwareX, vol. 17, p. e00511, 2024, doi: 10.1016/j.ohx.2024.e00511.
A. Alessandrini, “How an undergraduate group of design students solved wiring errors during the prototyping of an interactive artifact,” in Proc. Eur. Conf. Cogn. Ergon. (ECCE), Kaiserslautern, Germany, Oct. 2022, doi: 10.1145/3552327.3552345.
J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th ed. Thousand Oaks, CA, USA: SAGE Publications, 2014.
L. Cohen, L. Manion, and K. Morrison, Research Methods in Education, 8th ed. London, U.K.: Routledge, 2018.
I. Etikan, S. A. Musa, and R. S. Alkassim, “Comparison of convenience sampling and purposive sampling,” Am. J. Theor. Appl. Stat., vol. 5, no. 1, pp. 1–4, 2016, doi: 10.11648/j.ajtas.20160501.11.
D. Ariyanto, “Penggunaan Mesin CNC untuk Mengatasi Limbah B3 (Bahan Beracun dan Berbahaya) dalam Proses Pembuatan PCB,” J. Inform. dan Tek. Elektro Terap., vol. 13, no. 3, 2025, doi: 10.23960/jitet.v13i3.6767.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
