Roll-to-roll(R2R) peeling is an innovative method that transfers flexible electronics and 2D materials from the flexible substrate where they are grown to the end-use substrate. This
process enables the full potential of R2R 2D material fabrication methods in a continuous, high-throughput, and environment friendly manner. During the R2R peeling process, the device patterning causes periodic changes in the adhesion energy between the device and substrate. This periodic disturbance can degrade the quality of the final product if not properly controlled. Current control methods used for the R2R peeling process do not explicitly reject the periodic disturbance. It is therefore desirable to develop a controller that is capable of performing periodic disturbance rejection. This paper presents a model-based repetitive controller that integrates a frequency estimation of the disturbance into the R2R peeling control to maintain the optimal peeling process performance. A linear
estimator using system identification techniques is employed. The simulation results show that the developed controller achieves better R2R process performance when compared to a conventional model-based controller.
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Continuous roll-to-roll patterning of three-dimensional periodic nanostructures
In this work, we introduce a roll-to-roll system that can continuously print three-dimensional (3D) periodic nanostructures over large areas. This approach is based on Langmuir-Blodgett assembly of colloidal nanospheres, which diffract normal incident light to create a complex intensity pattern for near-field nanolithography. The geometry of the 3D nanostructure is defined by the Talbot effect and can be precisely designed by tuning the ratio of the nanosphere diameter to the exposure wavelength. Using this system, we have demonstrated patterning of 3D photonic crystals with a 500 nm period on a 50 × 200 mm2flexible substrate, with a system throughput of 3 mm/s. The patterning yield is quantitatively analyzed by an automated electron beam inspection method, demonstrating long-term repeatability of an up to 88% yield over a 4-month period. The inspection method can also be employed to examine pattern uniformity, achieving an average yield of up to 78.6% over full substrate areas. The proposed patterning method is highly versatile and scalable as a nanomanufacturing platform and can find application in nanophotonics, nanoarchitected materials, and multifunctional nanostructures.
more » « less- NSF-PAR ID:
- 10154409
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Microsystems & Nanoengineering
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2055-7434
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Modern high-throughput nanopatterning techniques, such as nanoimprint lithography, make it possible to fabricate arrays of nanostructures (features with dimensions of 10’s to 100’s of nm) over large area substrates (cm2to m2scale) such as Si wafers, glass sheets, and flexible roll-to-roll webs. The ability to make such large-area nanostructure arrays (LNAs) has created an extensive design space, enabling a wide array of applications including optical devices, such as wire-grid polarizers, transparent conductors, color filters, and anti-reflection surfaces, and building blocks for electronic components, such as ultracapacitors, sensors, and memory storage architectures. However, existing metrology methods will have trouble scaling alongside fabrication methods. Scanning electron microscopy (SEM) and atomic force microscopy (AFM), for instance, have micron scale fields of view (FOV) that preclude comprehensive characterization of LNAs, which may be manufactured at m2per minute rates. Scatterometry approaches have larger FOVs (typically 100’s of µm to a few mm), but traditional scatterometry systems measure samples one point at a time, which also makes them too slow for large-scale LNA manufacturing. In this work, we demonstrate parallelization of the traditional spectroscopic scatterometry approach using hyperspectral imaging, increasing the throughput of the technique by a factor of 106-107. We demonstrate this approach by using hyperspectral imaging and inverse modeling of reflectance spectra to derive 3-dimensional geometric data for Si nanopillar array structures over both mm and cm-scale with µm-scale spatial resolution. This work suggests that geometric measurements for a variety of LNAs can be performed with the potential for high speed over large areas which may be critical for future LNA manufacturing.
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Abstract A non‐volatile conjugated polymer‐based electrochemical memristor (cPECM), derived from sodium 4‐[(2,3‐dihydrothieno[3,4‐b][1,4]dioxin‐2‐yl)methoxy]butane‐2‐sulfonate (S‐EDOT), is fabricated through roll‐to‐roll printing and exhibited neuromorphic properties. The 3‐terminal device employed a “read” channel where conductivity of the water‐soluble, self‐doped S‐PEDOT is equated to synaptic weight and was electrically decoupled from the programming electrode. For the model system, a +2500 mV programming pulse of 100 ms duration resulted in a 0.136 μS resolution in conductivity change, giving over 1000 distinct conductivity states for one cycle. The minimum programming power requirements of the cPECM was 0.31 pJ mm−2and with advanced printing techniques, a 0.1 fJ requirement for a 20 μm device is achievable. The mathematical operations of addition, subtraction, multiplication, and division are demonstrated with a single cPECM, as well as the logic gates AND, OR, NAND, and NOR. This demonstration of a printed cPECM is the first step toward the implementation of a mass produced electrochemical memristor that combines information storage and processing and may allow for the realization of printable artificial neural networks.
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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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