Lee Jongsu, Kim Chung Hwan
Department of Advanced Components and Materials Engineering, Sunchon National University, 255 Jungang-ro, Suncheon 57922, Republic of Korea.
Department of Mechanical Engineering Education, Chungnam National University, 99 Daehak-ro, Daejeon 34134, Republic of Korea.
Nanomaterials (Basel). 2023 May 10;13(10):1597. doi: 10.3390/nano13101597.
In nanoparticle-based printed electronic devices, the printability of the patterns constituting the device are crucial factors. Although many studies have investigated the printability of patterns, only a few have analyzed and established international standards for measuring the dimensions and printability of shape patterns. This study introduces an advanced algorithm for accurate measurement of the geometry and printability of shape patterns to establish an international standard for pattern dimensions and printability. The algorithm involves three core concepts: extraction of edges of printed patterns and identification of pixel positions, identification of reference edges via the best-fitting of the shape pattern, and calculation of different pixel positions of edges related to reference edges. This method enables the measurement of the pattern geometry and printability, including edge waviness and widening, while considering all pixels comprising the edges of the patterns. The study results revealed that the rectangle and circle patterns exhibited an average widening of 3.55% and a maximum deviation of 1.58%, based on an average of 1662 data points. This indicates that the algorithm has potential applications in real-time pattern quality evaluation, process optimization using statistical or AI-based methods, and foundation of International Electrotechnical Commission standards for shape patterns.
在基于纳米颗粒的印刷电子器件中，构成器件的图案的可印刷性是关键因素。尽管许多研究已经调查了图案的可印刷性，但只有少数研究分析并建立了用于测量形状图案尺寸和可印刷性的国际标准。本研究引入了一种先进算法，用于精确测量形状图案的几何形状和可印刷性，以建立图案尺寸和可印刷性的国际标准。该算法涉及三个核心概念：提取印刷图案的边缘并识别像素位置、通过形状图案的最佳拟合识别参考边缘以及计算与参考边缘相关的边缘的不同像素位置。这种方法能够测量图案的几何形状和可印刷性，包括边缘波纹度和加宽，同时考虑构成图案边缘的所有像素。研究结果表明，基于1662个数据点的平均值，矩形和圆形图案的平均加宽率为3.55%，最大偏差为1.58%。这表明该算法在实时图案质量评估、使用统计或基于人工智能的方法进行工艺优化以及建立国际电工委员会形状图案标准方面具有潜在应用。
