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Roll-to-Roll (R2R) printing techniques are promising for high-volume continuous production of substrate-based products, as opposed to sheet-to-sheet (S2S) approach suited for low-volume work. However, meeting the tight alignment tolerance requirements of additive multi-layer printed electronics specified by device resolution that is usually at micrometer scale has become a major challenge in R2R flexible electronics printing, preventing the fabrication technology from being transferred from conventional S2S to high-speed R2R production. Print registration in a R2R process is to align successive print patterns on the flexible substrate and to ensure quality printed devices through effective control of various process variables. Conventional model-based control methods require an accurate web-handling dynamic model and real-time tension measurements to ensure control laws can be faithfully derived. For complex multistage R2R systems, physics-based state-space models are difficult to derive, and real-time tension measurements are not always acquirable. In this paper, we present a novel data-driven model predictive control (DD-MPC) method to minimize the multistage register errors effectively. We show that the DD-MPC can handle multi-input and multi-output systems and obtain the plant model from sensor data via an Eigensystem Realization Algorithm (ERA) and Observer Kalman filter identification (OKID) system identification method. In addition, the proposed control scheme works for systems with partially measurable system states.

 

Personalized healthcare (PHC) is a booming sector in the health science domain wherein researchers from diverse technical backgrounds are focusing on the need for remote human health monitoring. PHC employs wearable electronics, viz. group of sensors integrated on a flexible substrate, embedded in the clothes, or attached to the body via adhesive. PHC wearable flexible electronics (FE) offer numerous advantages including being versatile, comfortable, lightweight, flexible, and body conformable. However, finding the appropriate mass manufacturing technologies for these PHC devices is still a challenge. It needs an understanding of the physics, performance, and applications of printing technologies for PHC wearables, ink preparation, and bio-compatible device fabrication. Moreover, the detailed study of the operating principle, ink, and substrate materials of the printing technologies such as inkjet printing will help identify the opportunities and emerging challenges of applying them in manufacturing of PHC wearable devices. In this article, we attempt to bridge this gap by reviewing the printing technologies in the PHC domain, especially inkjet printing in depth. This article presents a brief review of the state-of-the-art wearable devices made by various printing methods and their applications in PHC. It focuses on the evaluation and application of these printing technologies for PHC wearable FE devices, along with advancements in ink preparation and bio-compatible device fabrication. The performance of inkjet, screen, gravure, and flexography printing, as well as the inks and substrates, are comparatively analyzed to aid PHC wearable sensor design, research, fabrication, and mass manufacturing. Moreover, it identifies the application of the emerging mass-customizable printing technologies, such as inkjet printing, in the manufacturing of PHC wearable devices, and reviews the printing principles, drop generation mechanisms, ink formulations, ink-substrate interactions, and matching strategies for printing wearable devices on stretchable substrates. Four surface matching strategies are extracted from literature for the guidance of inkjet printing of PHC stretchable electronics. The electro-mechanical performance of the PHC FE devices printed using four surface matching strategies is comparatively evaluated. Further, the article extends its review by describing the scalable integration of PHC devices and finally presents the future directions of research in printing technologies for PHC wearable devices. 

A suspended nanowire is used to track both the electrical and mechanical activities in cells. 

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.