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1. Physics-Informed Machine Learning for Accelerated Testing of Roll-to-Roll Printed Sensors

   https://doi.org/10.1115/MSEC2022-85392  

   Chandra Mouli, S.; Sedaghat, Sotoudeh; Ramazan Oduncu, Muhammed; Saha, Ajanta; Rahimi, Rahim; A. Alam, Muhammad; Wei, Alexander; Shakouri, Ali; Ribeiro, Bruno (June 2022, International Manufacturing Science and Engineering Conference)

   Roll-to-roll printing has significantly shortened the time from design to production of sensors and IoT devices, while being cost-effective for mass production. But due to less manufacturing tolerance controls available, properties such as sensor thickness, composition, roughness, etc., cannot be precisely controlled. Since these properties likely affect the sensor behavior, roll-to-roll printed sensors require validation testing before they can be deployed in the field. In this work, we improve the testing of Nitrate sensors that need to be calibrated in a solution of known Nitrate concentration for around 1–2 days. To accelerate this process, we observe the initial behavior of the sensors for a few hours, and use a physics-informed machine learning method to predict their measurements 24 hours in the future, thus saving valuable time and testing resources. Due to the variability in roll-to-roll printing, this prediction task requires models that are robust to changes in properties of the new test sensors. We show that existing methods fail at this task and describe a physics-informed machine learning method that improves the prediction robustness to different testing conditions (≈ 1.7× lower in real-world data and ≈ 5× lower in synthetic data when compared with the current state-of-the-art physics-informed machine learning method). 

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2. Data-Driven Model Predictive Control for Roll-to-Roll Process Register Error

   https://doi.org/10.1115/IAM2022-96840  

   Shah, Karan; He, Anqi; Wang, Zifeng; Du, Xian; Jin, Xiaoning (October 2022, Proceedings of the 2022 International Additive Manufacturing Conference. 2022 International Additive Manufacturing Conference. Lisbon, Portugal.)

   Abstract 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. 

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3. Large‐Scale Manufacturing of Pattern‐Integrated Paper Li‐Ion Microbatteries through Roll‐to‐Roll Flexographic Printing

   https://doi.org/10.1002/admt.202200303  

   Wang, Ying; Cao, Daxian; Sun, Xiao; Ren, Haoze; Ji, Tongtai; Jin, Xiaoning; Morse, Jeffrey; Stewart, Barbara; Zhu, Hongli (May 2022, Advanced Materials Technologies)

   Abstract Electrode architectures significantly influence the electrochemical performance, flexibility, and applications of lithium‐ion batteries (LiBs). However, the conventional bar coating for fabricating electrodes limits the addition of customized architecture or patterns. In this study, as a novel approach, patterns are integrated into electrodes through large‐scale roll‐to‐roll (R2R) flexographic printing. Additionally, flexible, recyclable, and biodegradable paper are innovatively used as a printing substrate during printing LiBs manufacturing, which exhibited superior printability. Moreover, the paper is modified with a thin‐layer Al₂O₃to function as the separator in the printed LiB. The Al₂O₃‐coated paper enables an admirable wettability for printing, excellent mechanical properties for high‐speed R2R manufacturing, and outstanding thermal stability for the safe and stable operation of LiBs. The assembled paper cells exhibit nearly 100% discharge capacity retention after 1000 cycles at 3 C and outstanding rate performance. This work inspires future large‐scale microbatteries manufacturing integrated with high‐resolution architecture designs. 

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4. A Dynamic System Model for Roll-to-Roll Dry Transfer of Two-Dimensional Materials and Printed Electronics

   https://doi.org/10.1115/1.4054187  

   Zhao, Qishen; Hong, Nan; Chen, Dongmei; Li, Wei (July 2022, Journal of Dynamic Systems, Measurement, and Control)

   Abstract Roll-to-roll (R2R) dry transfer is an important process for manufacturing of large-scale two-dimensional (2D) materials and printed flexible electronics. Existing research has demonstrated the feasibility of dry transfer of 2D materials in a roll-to-roll setting with mechanical peeling. However, the process presents a significant challenge to system control due to the lack of understanding of the mechanical peeling behavior and the complexity of the nonlinear system dynamics. In this study, an R2R peeling process model is developed to understand the dynamic interaction among the peeling process parameters, including adhesion energy, peeling force, angle, and speed. Both simulation and experimental studies are conducted to validate the model. It is shown that the dynamic system model can capture the transient behavior of the R2R mechanical peeling process and be used for the process analysis and control design. 

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5. High-speed in-line optical angular scatterometer for high-throughput roll-to-roll nanofabrication

   https://doi.org/10.1117/12.2593730  

   Faria-Briceno, Juan J; Sasidharan, Vineeth; Neumann, Alexander; Singhal, Shrawan; Sreenivasan, SV; Brueck, Steven_R J (February 2021, Proceedings of SPIE the International Society for Optical Engineering)

   Panning, Eric M; Liddle, J Alexander (Ed.)

   Roll-to-roll and other high-speed printing manufacturing processes are increasingly being extended to micro- and nanoelectronics and photonics due to cost and throughput advantages as compared with traditional wafer-scale manufacturing. The extra degrees of freedom associated with a moving web require high speed, in-line metrology to control the manufacturing process. Many state-of-the-art metrology approaches have sub-10 nm resolution but cannot be implemented during real-time fabrication processes because of environmental constraints or contact/cross sectional requirements. Optical angular scatterometry is a non-contact metrology approach that can be implemented at high speed. We demonstrate a system that uses 45° off-axis parabolic mirrors and an 8kHz resonant scanner to vary the incident/reflected angle from ~17° to ~67°, suitable for nanoscale metrology at web speeds of up to 350 cm/s, well-beyond the speed of current manufacturing tools. Scatterometry is sensitive not only to gross defects (missing pattern sections) but also to variations in nanoscale pattern details, offering a pathway to feedback control of the manufacturing process. 

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