Search

Wire bonding remains one of the most widely adopted interconnection techniques in the field of electronic packaging. At present, the most effective way to ensure a long life and high reliability of wire bonds is to improve the bonding quality. In this study, both experiments and finite element analysis (FEA) were employed to develop a fundamental understanding of wire bond degradation. Sensors with two protective silicone gels were loaded with the same thermal shock at a temperature ranging from $- 40^{\circ }\text{C}$ to 125°C, and the switching time was shorter than 10 s. The number of thermal shock cycles for the aluminum wire covered with transparent silicone reached 1200, but the maximum number of cycles for the other wire only reached 454. The experimental results indicated that the chosen transparent silicone performed better than did the black silicone originally selected, which was also verified by the simulation results. In addition, bond pull and shear tests were performed. The results revealed no degradation of either the Ag-Al or Ni-Al bonding joints under thermal loading. In summary, the root cause of failure was found to be improper protection silicone application, which, as often ignored in analysis, accelerated thermal fatigue of the aluminum wires. An explanation of the observed trend and a recommended aluminum wire bonding method were also provided.

Tire pressure monitoring is essential to driving safety. Indirect tire pressure monitoring system (TPMS) is a cost-effective alternative to direct tire pressure monitoring system. Its performance depends on the algorithm for data pre-processing and analysis which is normally complicated, sensitive to initial calibration with limited working range. In this work, four tests were carried out with Baojun 530 with a different deflated tire in each case. Speed data read through ABS CAN bus was analyzed and traditional frequency based method was employed to identify the deflated tire. To simplify the data pre-processing and improve response speed and working range, a new artificial neural network (ANN) based method was also proposed to identify deflated tire based on speed data point collected through antilock brake system (ABS) sensors in tests. A long short-term memory (LSTM) network was developed to locate the deflated tire with an accuracy of 0.83 after training for individual data points. And performance of this method can be further improved by employing a soft voting mechanism with 3 LSTM networks. In proposed ANN based method, benchmark data from properly inflated tire is not required, which makes it a promising solution for multiple deflated tires cases which is challenging for traditional frequency-based method.