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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. 

We propose a fast and accurate autofocus algorithm using Gaussian standard deviation and gradient-based binning. Rather than iteratively searching for the optimal focus using an optimization process, the proposed algorithm directly calculates the mean of the Gaussian shaped focus measure (FM) curve to find the optimal focus location and uses the FM curve standard deviation to adapt the motion step size. The calculation only requires 3-4 defocused images to identify the center location of the FM curve. Furthermore, by assigning motion step sizes based on the FM curve standard deviation, the magnitude of the motion step is adaptively controlled according to the defocused measure, thus avoiding overshoot and unneeded image processing. Our experiment verified the proposed method is faster than the state-of-the-art Adaptive Hill-Climbing (AHC) and offers satisfactory accuracy as measured by root-mean-square error. The proposed method requires 80% fewer images for focusing compared to the AHC method. Moreover, due to this significant reduction in image processing, the proposed method reduces autofocus time to completion by 22% compared to the AHC method. Similar performance of the proposed method was observed in both well-lit and low-lighting conditions. 

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. 

Luminance and irradiance uniform ring-light is essential for image processing and machine vision for measure scopes. A curved lens is designed for irradiance uniformity and five types of wavelengths of LEDs are used to fulfill luminance uniformity.