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1. The Line Speed Effect in Roll-to-Roll Dry Transfer of Chemical Vapor Deposition Graphene,

   Hong, N. (December 2021, 2021 International Conference on Micro- and Nano-devices Enabled by R2R Manufacturing)

   Sreenivasan, S.V. (Ed.)

   A roll-to-roll (R2R) technique is especially desirable for transfer of chemical vapor deposition (CVD) graphene towards high-speed, low-cost, renewable, and environmentally friendly manufacturing of graphene-based electronic devices, such as flexible touchscreens, field effect transistors and organic solar cells. A R2R graphene dry transfer system is recently developed. Monolayer graphene is transferred from a copper growth substrate to a polymer backing layer by mechanical peeling. In this work, we present an experimental study to examine the effects of line speed of the mechanical peeling process on the transferred graphene quality. It is shown that the effect of line speed is not monotonic, and an optimal speed exists to yield the highest and most consistent electrical conductivity of transferred graphene among the process conditions studied. This study provides understanding of process parameter effects and demonstrates the potential of the R2R dry transfer process for large-scale CVD graphene toward industrial applications. 

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2. Tension Control in Roll-to-Roll Mechanical Peeling for 2D Material Transfer and Transfer Printing

   Zhao, Q. (December 2021, 2021 International Conference on Micro- and Nano-devices Enabled by R2R Manufacturing)

   Sreenivasan, S.V. (Ed.)

   A major challenge of the large-scale application of two-dimensional (2D) materials is the scaling up of the process for its growth and transfer. Mechanical peeling has been demonstrated to be a promising method for transferring graphene in a fast and environmentally friendly manner. However, efforts in scaling up the process have been lacking. Performing mechanical peeling using a roll-to-roll (R2R) system could significantly increase the throughput of graphene transfer. Such a R2R process does not exist in industry. In this paper a novel R2R mechanical peeling system that has both speed and tension control capabilities is presented. Controllers that control the peeling tensions on both sides of the peeling front are developed based on a tension dynamics model. Both controllers contain a feedback and a feedforward term to account for large steady-state error. The control performance is validated using both experiments and simulation, demonstrating that the R2R mechanical peeling technique can be a viable method for dry transfer of 2D materials in a high-throughput industrial setting. 

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3. 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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4. Optimal Control of a Roll-to-Roll Dry Transfer Process With Bounded Dynamics Convexification

   https://doi.org/10.1115/1.4066456  

   Martin, Christopher; Zhao, Qishen; Bakshi, Soovadeep; Velasquez, Enrique; Li, Wei; Chen, Dongmei (May 2025, Journal of Dynamic Systems, Measurement, and Control)

   Abstract For efficient roll-to-roll (R2R) production of flexible electronic components, a precise R2R transfer peeling process is essential, requiring accurate modeling and control. This paper introduces a novel approach to confining the dynamics of a nonlinear R2R mechanical peeling system within a convex set known as a norm-bounded linear differential inclusion (NLDI). This method utilizes constraints on uncertain system variables to create a tighter NLDI representation compared to other convexification techniques. Moreover, it offers drastically reduced computational cost compared to previous methods applied to convexify the R2R peeling system. The NLDI is employed to generate an H∞-optimal controller for the R2R peeling system, and both simulations and experiments demonstrate better dynamic performance compared to other controllers for R2R transfer. 

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5. 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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