The field of printed electronics has been extensively researched for its versatility and scalability in flexible and large-area applications.Impedance is of great importance for the performance and reliability of elec...The field of printed electronics has been extensively researched for its versatility and scalability in flexible and large-area applications.Impedance is of great importance for the performance and reliability of electronics.However,its measurement requires electrical contacts,which makes it difficult on complex or bio-interfaces.Although the printing process is accessible,impedance characterization may be cumbersome,which can create a bottleneck during the manufacturing process.This paper reports the first effort at developing a convolutional neural network(CNN)based image regression model to replace impedance spectroscopy(IS).In our study,the CNN model learned the features of inkjet-printed electrode images that are dependent on the printing and sintering of nanomaterials and quantitatively predicted the resistance and capacitance of the equivalent circuit of the inkjet-printed lines.The image-based impedance spectroscopy(IIS)is expected to be the cornerstone as a revolutionary approach to electronics research and development enabled by deep neural networks.展开更多
基金supported by the Ministry of Education through the Basic Science Research Program through the National Research Foundation of Korea(NRF-2021R1I1A3059714)by the Korea Institute of Industrial Technology as"Development of root technology for multi-product flexible production(KITECH EO-24-0009)+1 种基金supported by project for Collabo R&Dbetween Industry,University,and Research Institute funded by Korea Ministry of SMEs and Startups in 2023(RS-2023-00224114)supported by the faculty research fund of Sejong University in 2024。
文摘The field of printed electronics has been extensively researched for its versatility and scalability in flexible and large-area applications.Impedance is of great importance for the performance and reliability of electronics.However,its measurement requires electrical contacts,which makes it difficult on complex or bio-interfaces.Although the printing process is accessible,impedance characterization may be cumbersome,which can create a bottleneck during the manufacturing process.This paper reports the first effort at developing a convolutional neural network(CNN)based image regression model to replace impedance spectroscopy(IS).In our study,the CNN model learned the features of inkjet-printed electrode images that are dependent on the printing and sintering of nanomaterials and quantitatively predicted the resistance and capacitance of the equivalent circuit of the inkjet-printed lines.The image-based impedance spectroscopy(IIS)is expected to be the cornerstone as a revolutionary approach to electronics research and development enabled by deep neural networks.
