Skip to main navigation menu Skip to main content Skip to site footer

MODEL OF THE TECHNOLOGICAL PROCESS OF WEDGE BONDING BASED ON FEK DELVOTEC 6400 IN THE PRODUCTION OF HYBRID ELECTRONIC MODULES

PDF

Abstract

Modern development of microelectronics and hybrid electronic modules is characterized by continuously increasing requirements for the accuracy, reliability, and stability of interconnections in the manufacturing process of electronic components. One of the most widely used technologies for forming electrical contacts is thin aluminum wire wedge bonding, which ensures high connection quality and the possibility of manufacturing process automation.


References

  1. Luo, Z., Wang, X., Li, Y., Zhou, P., Chen, L., Bai, C., ... & Ji, H. (2026). Optimization and reliability of ultrasonic wedge bonding performance of copper wires on gold pads for MEMS devices. Microelectronics Reliability, 176, 115971. https://doi.org/10.1016/j.microrel.2025.115971
  2. Zhang, Y., Cao, R., Liang, Q., & Zhang, S. (2025, March). Effect of ultrasonic power on the morphology evolution of wedge bonded interface between Al and Cu wires. In Journal of Physics: Conference Series (Vol. 2956, No. 1, p. 012012). IOP Publishing. https://doi.org/10.1088/1742-6596/2956/1/012012
  3. Luo, B., Saad, M., & Hanif, M. (2025). Optimization and Analysis of Ultrasonic Wedge Bonding Parameters for Enhanced Bonding Performance in 21700 Cylindrical Lithium Battery Modules. Pertanika Journal of Science & Technology, (3). https://doi.org/10.47836/pjst.33.3.18
  4. Pant, U., Banerjee, S., & Kundu, T. (2025). Imaging of debonds in a FRP strengthened concrete beam using linear and nonlinear features of surface guided waves generated by a wedge transducer. Ultrasonics, 150, 107600. https://doi.org/10.1016/j.ultras.2025.107600
  5. Li, Y., Pan, B., Ge, Z., Chen, P., Bi, B., Yi, X., ... & Wang, C. (2025). Soldering and bonding in contemporary electronic device packaging. Materials, 18(9), 2015. https://doi.org/10.3390/ma18092015
  6. Abu-Jassar, A., Attar, H., Hafez, M., Hamdan, M., Yevsieiev, V., & Lyashenko, V. (2025, December). Adaptive System for Constructing a Mobile Robot Trajectory Using Neural Network and Stochastic Optimizer. In 2025 26th International Arab Conference on Information Technology (ACIT) (pp. 422-428). IEEE. https://doi.org/10.1109/ACIT68900.2025.11510567
  7. Abu-Jassar, A., Attar, H., Hafez, M., Hamdan, M., Yevsieiev, V., & Lyashenko, V. (2025, December). Adaptive System for Constructing a Mobile Robot Trajectory Using Neural Network and Stochastic Optimizer. In 2025 26th International Arab Conference on Information Technology (ACIT) (pp. 422-428). IEEE. https://doi.org/10.1109/ACIT68900.2025.11510567
  8. Yevsieiev, V., Gurin, D., Kulish, S., & Voloshyn, Y. (2025). Development of a partially supervised Markov decision-making model for a 3-link collaborative robot-manipulator. Radioelectronic and Computer Systems, 2025(4), 83-94. doi:https://doi.org/10.32620/reks.2025.4.06

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.