Shakeel Anood, Maskey Bijendra Bishow, Shrestha Sagar, Parajuli Sajjan, Jung Younsu, Cho Gyoujin
Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon-si 16419, Republic of Korea.
Department of Biophysics, Institute of Quantum Biophysics, Research Engineering Center for R2R Printed Flexible Computer, Sungkyunkwan University, Suwon-si 16419, Republic of Korea.
Nanomaterials (Basel). 2023 Mar 10;13(6):1008. doi: 10.3390/nano13061008.
Roll-to-roll gravure (R2Rg) has become highly affiliated with printed electronics in the past few years due to its high yield of printed thin-film transistor (TFT) in active matrix devices, and to its low cost. For printing TFTs with multilayer structures, achieving a high-precision in overlay printing registration accuracy (OPRA) is a key challenge to attain the high degree of TFT integration through R2Rg. To address this challenge efficiently, a digital twin paradigm was first introduced in the R2Rg system with an aim to optimize the OPRA by developing a predictive model based on typical input variables such as web tension, nip force, and printing speed in the R2Rg system. In our introductory-level digital twin, errors in the OPRA were collected with the variable parameters of web tensions, nip forces, and printing speeds from several R2Rg printing processes. Subsequently, statistical features were extracted from the input data followed by the training of a deep learning long-short term memory (LSTM) model for predicting machine directional error (MD) in the OPRA. As a result of training the LSTM model in our digital twin, its attained accuracy of prediction was 77%. Based on this result, we studied the relationship between the nip forces and printing speeds to predict the MD error in the OPRA. The results indicated a correlation between the MD error in the OPRA and the printing speed, as the MD error amplitude in the OPRA tended to decline at the higher printing speed.
在过去几年中，卷对卷凹版印刷（R2Rg）因其在有源矩阵器件中印刷薄膜晶体管（TFT）的高产量以及低成本，与印刷电子学紧密相连。对于印刷具有多层结构的TFT而言，在套印配准精度（OPRA）方面实现高精度是通过R2Rg实现高度TFT集成的关键挑战。为了有效应对这一挑战，数字孪生范式首次被引入到R2Rg系统中，旨在通过基于R2Rg系统中的典型输入变量（如卷材张力、压区力和印刷速度）开发预测模型来优化OPRA。在我们的入门级数字孪生中，通过几个R2Rg印刷过程中卷材张力、压区力和印刷速度的可变参数来收集OPRA中的误差。随后，从输入数据中提取统计特征，接着训练一个深度学习长短期记忆（LSTM）模型来预测OPRA中的机器方向误差（MD）。在我们的数字孪生中对LSTM模型进行训练的结果是，其预测准确率达到了77%。基于这一结果，我们研究了压区力和印刷速度之间的关系，以预测OPRA中的MD误差。结果表明，OPRA中的MD误差与印刷速度之间存在相关性，因为在较高印刷速度下，OPRA中的MD误差幅度趋于下降。
