Among many attempts to make a decent human motion detector in various engineering fields, a mechanical crack-based sensor that deliberately generates and uses nano-scale cracks on a metal deposited thin film is gaining attention for its high sensitivity. While the metal layer of the sensor must be responsible for its high performance, its effects have not received much academic interest. In this paper, we studied the relationship between the thickness of the metal layer and the characteristics of the sensor by depositing a few nanometers of chromium (Cr) and gold (Au) on the PET film. We found that the sensitivity of the crack sensor improves/increases under the following conditions: (1) when Au is thin and Cr is thick; and (2) when the ratio of Au is lower than that of Cr, which also increases the transmittance of the sensor, along with its sensitivity. As we only need a small amount of Au to achieve high sensitivity of the sensor, we have suggested more efficient and economical fabrication methods. With this crack-based sensor, we were able to successfully detect finger motions and to distinguish various signs of American Sign Language (ASL).
Funding: This material is based upon work supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No. 20000512, “Development of Task Planning, Gripping and Manipulation Technologies of Deformable Objects based on Machine Learning for Manufacturing and Logistical Process”. Daeshik Kang, Seungyong Han, and Je-sung Koh acknowledge financial support by the new faculty research fund of Ajou University and the Ajou University research fund. Daeshik Kang acknowledges financial support by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2016R1C1B1009689).This material is based upon work supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No. 20000512, “Development of Task Planning, Gripping and Manipulation Technologies of Deformable Objects based on Machine Learning for Manufacturing and Logistical Process”. Daeshik Kang, Seungyong Han, and Je-sung Koh acknowledge financial support by the new faculty research fund of Ajou University and the Ajou University research fund. Daeshik Kang acknowledges financial support by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2016R1C1B1009689). Acknowledgments: D.K. thanks Mansoo Choi of Seoul National University for his advice and support.