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- 저자 : Xu Suling (검색결과 2 건)
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Influence of thermal treatment on the performance of copper phthalocyanine thin-film transistors
Xueyan Tian,Zheng Xu,Fujun Zhang,Suling Zhao,Guangcai Yuan,Jing Li,Qinjun Sun,Ying Wang 한국물리학회 2010 Current Applied Physics Vol.10 No.1
Organic thin-film transistors (OTFTs) with bottom-gate and bottom-contact configuration based on copper phthalocyanines (CuPc) as active layer were fabricated. The performance of CuPc OTFTs was studied before and after thermal treatment on CuPc layer. The values of the threshold voltage before and after thermal treatment are -6.3 and -5.7 V, respectively. The field-effect mobility values in saturation regime of CuPc thin-film transistors before and after thermal treatment are 0.014 ㎠/Vs and 0.0068 ㎠/Vs,respectively. The experimental results indicate that there is a heavy decay on the mobility of CuPc based OTFTs mostly due to the crystalline morphology change induced by the thermal treatment, and absolute value of the threshold voltage after thermal treatment decreases with the decrease of the CuPc film thickness and the roughness.
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Hao Shuhui,Huang Changcheng,Heidari Ali Asghar,Chen Huiling,Li Lingzhi,Algarni Abeer D.,Elmannai Hela,Xu Suling 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.2
If found and treated early, fast-growing skin cancers can dramatically prolong patients’ lives. Dermoscopy is a convenient and reliable tool during the fore-period detection stage of skin cancer, so the efficient processing of digital images of dermoscopy is particularly critical to improving the level of a skin cancer diagnosis. Notably, image segmentation is a part of image preprocessing and essential technical support in the process of image processing. In addition, multi-threshold image segmentation (MIS) technology is extensively used due to its straightforward and effective features. Many academics have coupled different meta-heuristic algorithms with MIS to raise image segmentation quality. Nonetheless, these meta-heuristic algorithms frequently enter local optima. Therefore, this paper suggests an improved salp swarm algorithm (ILSSA) method that combines iterative mapping and local escaping operator to address this drawback. Besides, this paper also proposes the ILSSA-based MIS approach, which is triumphantly utilized to segment dermoscopic images of skin cancer. This method uses two-dimensional (2D) Kapur’s entropy as the objective function and employs non-local means 2D histogram to represent the image information. Furthermore, an array of benchmark function test experiments demonstrated that ILSSA could alleviate the local optimal problem more effectively than other compared algorithms. Afterward, the skin cancer dermoscopy image segmentation experiment displayed that the proposed ILSSA-based MIS method obtained superior segmentation results than other MIS peers and was more adaptable at different thresholds.
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