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Condensation figure (CF) is a simple and cost-effective method to inspect patterns and defects on product surfaces. This inspection method is based on energy differentials on surfaces. Due to wettability contrast, water droplets are preferentially nucleated and grown on hydrophilic regions. The formed CF can further be segmented for the recognition and measurement of the patterns on the surfaces. The state-of-the-art CF methods are closeenvironmental, while controlled open-environmental CF has broader applications in manufacturing and quality inspection. The lack of open-environmental CF for such applications is mostly because of the unavailable droplet size control methods. In this paper, we designed a high-resolution optical surface inspection system based on open environment droplet-size-controlled CFs. This is done by real-time imaging and recognizing the condensed droplet sizes and densities on surfaces, and accordingly tuning the vaporization and evaporation of droplets on the surface by the vapor flow rate. Our experimental results show that the average diameter of droplets can be controlled below 3.5 µm in a laboratory setup for different metal substrates. We also test the system for inspecting self-assembled monolayer patterns with linewidth of 5 µm on a gold surface; this can be promisingly used for online quality monitoring and in-process control of printed patterns in flexible devices manufacturing. 

The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process.