In this study, an image inspection method was introduced to two-arm Archimedean spiral antenna patterns to quantify and qualify their in-line integrity, which was linked to their off-line electrical characteristics in terms of the capacitance values through machine learning. The pattern was intentionally deteriorated in shape to imitate potential fabrication variations existing in the microelectronic production line, and six physical features including the inner line edge roughness (LER), outer LER, integrated LER, inner arm length, outer arm length, and arm area were collected. Two groups of training and testing samples were simulated and fabricated. Based on Gaussian process regression with the covariance function in the form of a squared exponential, a model was developed to predict the capacitance values from the performances of the six features. The accuracy of the developed model was evaluated using the coefficient of determination and root-mean-square error. The results indicate that the developed model is capable of predicting the off-line electrical characteristics of microelectronic components based on their in-line pattern integrities. Advanced studies also reveal that although all LER values and arm lengths contribute to the electrical characteristics, the arm area is decisive.

在这项研究中，将图像检查方法引入到两臂阿基米德螺旋天线图案中，以量化和验证其线内完整性，并通过机器学习将其与电容值相关的离线电特性联系起来。图案的形状被故意恶化，以模仿微电子生产线中潜在的制造变化，并具有六个物理特征，包括内部线边缘粗糙度（LER），外部LER，集成LER，内部臂长，外部臂长和臂面积被收集。模拟和制作了两组训练和测试样本。基于具有协方差函数的平方指数形式的高斯过程回归，开发了一个模型来根据六个功能部件的性能预测电容值。使用确定系数和均方根误差评估开发模型的准确性。结果表明，所开发的模型能够基于其在线图案完整性来预测微电子组件的离线电特性。先进的研究还表明，尽管所有LER值和臂长都有助于电气特性，但臂的面积是决定性的。