Heat-resistant, low-cost, and large-area flexible piezoelectric sheet sensors that detect the postures of drivers are required for practical driver monitoring systems for vehicles. However, typically used polymer-based piezoelectric materials have low heat resistance. Here, we present a simple heat-resistant piezoelectric sheet sensor based on solution-processed zinc oxide films and discuss its sensing performance. Piezoelectric lithium-doped zinc oxide (Zn1–x Li x O) films are directly prepared on polyimide substrates by a facile solution process, producing relatively large-area sheet sensors with a simple structure and high electrical resistance. The solution-processed Zn1–x Li x O films have a c-axis-oriented wurtzite structure and exhibit an inherent piezoelectric response probably owing to its preferential orientation along the O-polar grains in the self-organized wurtzite films during the formation process. The piezoelectric response of the sheet sensors is unaffected by a heat-resistance test conducted at 393 K. When the sheet sensors are attached to chairs, they are sufficiently sensitive to detect the vital signs (such as respiration and pulse rates), body movements, and countermovements of a seated subject in a laboratory setting. This work demonstrates the feasibility of an unobtrusive driver monitoring system in which heat-resistant piezoelectric sheet sensors are embedded in the seats of future smart vehicles.
Wire bonding is an important technique for forming semiconductor junctions, and statistical quality assurance is determined through sampling inspection. However, as the number of connections increases due to high semiconductor integration, wire bonding reliability becomes important, and connection evaluation is required for all products. In a previous study, we focused on the application of ultrasonic waves, which greatly influences the bonding strength of wire bonding, and proposed a quality estimation method using a thin film AE sensor and machine learning. In that study, samples made with manual wire bonders were destroyed by pull testing, and the quality was judged based on the fracture load. However, the loop shape of the manually prepared samples was not constant, and thus the results of the pull testing varied. In this study, we automated the fabrication of the bonding samples, stabilized the sample shape, and sought improvement in the quality evaluation performance. Since the number of defective samples was small, we developed a quality estimation method using a one-class SVM, an anomaly detection method involving machine learning. Experiments using actual samples confirmed that the accuracy rate in the proposed method was roughly 86%.
Ishida et al.: Sampling Inspection for Wire Bonding Using Taming and Thin Film AE Sensor (1/7) IntroductionVarious standard inspection methods have been proposed in order to ensure quality assurance. In practice, sampling inspection by using attributes which are indexed in terms of acceptance quality limit (AQL) is widely used.[1, 2] A quality control strategy which is acceptable for the customer and manufacturer is determined by statistical backing with sample data, then the sampling plan is selected. To obtain satisfactory determining performance requires a sufficient number of accurate datasets, but it generally causes an increase in inspection costs. Therefore, we attempt to actively and effectively use information obtained from "sample" which tends to be a relatively small but accurate data set, as well as from "uninspected items" which tend to be an abundant but a less accurate data set. "Uninspected items" refers to the items which remain in the lot after the samples are taken from the lot.The aim of this paper is to improve determining perfor-mance in quality control using these two concepts ("sample" and "uninspected items") in a mutually complementary combination. This approach is especially effective in sampling inspection which is performed by destructive testing with associated high costs. An example of a high cost sampling inspection is the quality assurance testing in wire bonding, which is a step in the semiconductor manufacturing process. In this paper, we present a quality control procedure which can contribute to a higher reliability of the semiconductor bonding process.Bond pull test, bond peel test, and bond share test are commonly used in destructive testing for reliability evaluation.[3] Some non-destructive inspections which improve efficiency and reduce costs have been proposed.[4] However, destructive sampling inspection has not been completely eliminated. It is therefore desirable to devise an approach for improving the determining performance without largely changing the existing equipment. With this
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