In this paper we investigate the influence of different metallic (or metal-based) masking materials and plasma techniques for etching and patterning polycrystalline boron-doped diamond thin films. Lift-off technique was used to prepare various testing mask patterns with dimensions ranging from 1 μm to 15 μm. The results of plasma etching utilizing 100 nm Al and Cu masks are compared. Radio frequency and inductively coupled pure oxygen plasma techniques were used to obtain the fine etched structures. A simple etching scheme describing the obtained results is presented for each type of plasma technique and mask type. Although the Al mask is widely believed to have outstanding properties over other metallic materials, we found that the Cu mask exhibits lower side edge etching for both types of plasma techniques. A formation of thick and crystalline copper oxide layer in contrast with thin amorphous aluminum oxide is believed to be the reason for this behavior. Consequently, the etched structures also possessed much better side wall angles which is a key parameter for micro-fabrication of boron doped diamond microelectrodes, microfluidics, sensors and microelectromechanical devices in general.
The manuscript proposes the new 3-step universal defect detection system U2S-CNN tuned with visual data containing gear wheel images. The main advantage of the system is the detection capability of even unknown patterns of defects occurring in datasets. The object detection and defect detection approaches differ significantly in the basic principle. The precisely specified objects or patterns are sought in object detection and in the case of defect detection, patterns of different shape, orientation, color, character, etc. are sought. The problem of searching unknown objects is solved by defining the correct areas on the controlled object by using an asymmetric autoencoder of our own design. Subsequently, the differences between the original and autoenconder generated image are produced. The differences are divided into clusters using the DBSCAN method. Based on the clusters, regions of interest are defined, which are then classified using the pretrained Xception network classifier fined tuned with our data. The result is a 3-phase system capable of focusing even on unknown defects not occurring in the dataset using the sequence of Unsupervised learning – Unsupervised learning – Supervised learning methods. The proposed system is inspired by similarly designed systems used for the detection of anomalies or tumors in MRI or CT images, where U-networks or autoencoders are used. From the point of view of the nature of the issue, these problems can be considered very similar.
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