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.
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.
more » « less- Award ID(s):
- 1907250
- NSF-PAR ID:
- 10379858
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials Technologies
- Volume:
- 7
- Issue:
- 11
- ISSN:
- 2365-709X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Roll-to-roll (R2R) printing techniques are promising for high-volume continuous production of substrate-based electronic products, as opposed to the sheet-to-sheet approach suited for low-volume work. However, one of the major challenges in R2R flexible electronics printing is achieving tight alignment tolerances, as specified by the device resolution (usually at micrometer level), for multi-layer printed electronics. The alignment of the printed patterns in different layers, known as registration, is critical to product quality. Registration errors are essentially accumulated positional or dimensional deviations caused by un-desired variations in web tensions and web speeds. Conventional registration control methods rely on model-based feedback controllers, such as PID control, to regulate the web tension and the web speed. However, those methods can not guarantee that the registration error always converges to zero due to lagging problems. In this paper, we propose a Spatial-Terminal Iterative Learning Control (STILC) method combined with PID control to enable the registration error to converge to zero iteratively, which achieves unprecedented control in the creation, integration and manipulation of multi-layer microstructures in R2R processes. We simulate the registration error generation and accumulation caused by axis mismatch between roller and motor that commonly exists in R2R systems. We show that the STILC-PID hybrid control method can eliminate the registration error completely after a reasonable number of iterations. We also compare the performances of STILC with a constant-value basis and a cosine-form basis. The results show that the control model with a cosine-form basis provides a faster convergence speed for R2R registration error elimination.
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Abstract A major challenge for graphene applications is the lack of mass production technology for large‐scale and high‐quality graphene growth and transfer. Here, a roll‐to‐roll (R2R) dry transfer process for large‐scale graphene grown by chemical vapor deposition is reported. The process is fast, controllable, and environmentally benign. It avoids chemical contamination and allows the reuse of graphene growth substrates. By controlling tension and speed of the R2R dry transfer process, the electrical sheet resistance is achieved as 9.5 kΩ sq−1, the lowest ever reported among R2R dry transferred graphene samples. The R2R dry transferred samples are used to fabricate graphene‐based field‐effect transistors (GFETs) on polymer. It is demonstrated that these flexible GFETs feature a near‐zero doping level and a gate leakage current one to two orders of magnitude lower than those fabricated using wet‐chemical etched graphene samples. The scalability and uniformity of the R2R dry transferred graphene is further demonstrated by successfully transferring a 3 × 3 in2sample and measuring its field‐effect mobility with 36 millimeter‐scaled GFETs evenly spaced on the sample. The field‐effect mobility of the R2R dry transferred graphene is determined to be 205 ± 36 cm2 V−1.
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Roll-to-roll (R2R) printing techniques are promising for high-volume continuous production of substrate-based electronic products, as opposed to the sheet-to-sheet approach suited for low-volume work. However, one of the major challenges in R2R flexible electronics printing is achieving tight alignment tolerances, as specified by the device resolution (usually at micrometer level), for multi-layer printed electronics. The alignment of the printed patterns in different layers, known as registration, is critical to product quality. Registration errors are essentially accumulated positional or dimensional deviations caused by un-desired variations in web tensions and web speeds. Conventional registration control methods rely on model-based feedback controllers, such as PID control, to regulate the web tension and the web speed. However, those methods can not guarantee that the registration error always converges to zero due to lagging problems. In this paper, we propose a Spatial-Terminal Iterative Learning Control (STILC) method combined with PID control to enable the registration error to converge to zero iteratively, which achieves unprecedented control in the creation, integration and manipulation of multi-layer microstructures in R2R processes. We simulate the registration error generation and accumulation caused by axis mismatch between roller and motor that commonly exists in R2R systems. We show that the STILC-PID hybrid control method can eliminate the registration error completely after a reasonable number of iterations. We also compare the performances of STILC with a constant-value basis and a cosine-form basis. The results show that the control model with a cosine-form basis provides a faster convergence speed for R2R registration error elimination.more » « less
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