This roadmap includes the perspectives and visions of leading researchers in the key areas of flexible and printable electronics. The covered topics are broadly organized by the device technologies (sections 1–9), fabrication techniques (sections 10–12), and design and modeling approaches (sections 13 and 14) essential to the future development of new applications leveraging flexible electronics (FE). The interdisciplinary nature of this field involves everything from fundamental scientific discoveries to engineering challenges; from design and synthesis of new materials via novel device design to modelling and digital manufacturing of integrated systems. As such, this roadmap aims to serve as a resource on the current status and future challenges in the areas covered by the roadmap and to highlight the breadth and wide-ranging opportunities made available by FE technologies.

With the development of technology, wireless and IoT devices are increasingly used from daily life to industry, placing demands on rapid and efficient manufacturing processes. This study demonstrates the fabrication of an IoT device using a roll-to-roll printing process, which could shorten the device fabrication time and reduce the cost of mass production. Here, the fabricated IoT device is designed to acquire data through the sensor, process the data, and communicate with end-user devices via Bluetooth communication. For fabrication, a four-layer circuit platform consisting of two conductive layers, an insulating layer including through holes, and a solder resist layer is directly printed using a roll-to-roll screen printing method. After the printing of the circuit platform, an additional layer of solder paste is printed to assemble the electrical components into the device, inspiring the fully roll-to-roll process for device fabrication. Successful IoT device deployment opens the chance to broaden the roll-to-roll fabrication process to other flexible and multilayer electronic applications.

This study proposes the design of a roll-to-roll system for flexible electronics that enables accurate and precise tension control. It analyzes the factors for change in the tension of a roll-to-roll system and develops a tension model for each section to successfully predict the tension that is applied to such a system, the sagging of film according to tension, and deformation due to residual stress. This series of modeling processes allow engineers to design a roll-to-roll system for flexible electronics. Both a velocity control method for the tension between in-feeder and out-feeder—where there is no change of roller radius—and torque control method for the tension in modules like the rewinder, where the roll radius changes, are proposed. A roll-to-roll system according to the proposed design procedure and tension control methods was manufactured and experimented on under various test conditions. The accuracy and precision of velocity control between the in-feeder and the out-feeder were 100.01% and 1.15%, respectively, whereas those of torque control between the out-feeder and the rewinder were 99.9% and 1.35%, respectively, both at one sigma. The experiments confirmed that the two proposed types of tension control methods were accurate and precise. The experimental result with a monitoring sensor showed that the modeling was valid and that an accurate roll-to-roll system minimizing tension reduction was built. This study successfully demonstrated roll-to-roll system design and control techniques that are applicable to various pieces of roll-to-roll process equipment.

One of the advantages of inkjet printing in digital manufacturing is the ability to use multiple nozzles simultaneously to improve the productivity of the processes. However, the use of multiple nozzles makes inkjet status monitoring more difficult. The jetting nozzles must be carefully selected to ensure the quality of printed products, which is challenging for most inkjet processes that use multi-nozzles. In this article, we improved inkjet print head monitoring based on self-sensing signals by using machine learning algorithms. Specifically, supervised machine learning models were used to classify nozzle jetting conditions. For this purpose, the self-sensing signals were acquired, and the feature information was extracted for training. A vision algorithm was developed to label the nozzle status for classification. The trained models showed that the classification accuracy is higher than 99.6% when self-sensing signals are used for monitoring. We also proposed a so-called hybrid monitoring method using trained machine learning models, which divides the feature space into three regions based on predicted jetting probability: certain jetting, certain non-jetting, and doubt regions. Then, the nozzles with uncertain status in the doubt region can be verified by jet visualization to improve the accuracy and efficiency of the monitoring process.

Screen printing has been adopted for fabricating a wide variety of electronic devices. However, the printing defects and reliability have been an obstacle for industrialization of printed electronics. In this research, the artificial intelligence (AI) model was developed and integrated with the in-house roll-to-roll screen printing system to detect smearing defect, which is one of the main defects of screen printing. The U-Net architecture was adopted, and a total of 19 models were designed with model sizes ranging from 8E + 3 to 3E + 7 number of parameters. Their performances as validation mean Intersection over Union (IoU) were analyzed, and the optimal model was chosen with a validation mean IoU of 95.1% and a number of parameters of 8E + 6. The printed line images were evaluated by the AI model for various printing conditions, such as printed line widths, printing paste premixing, printing speeds, and printed line directions, which showed that the model could effectively detect the smearing defects. Also, the AI model capabilities were investigated for repeated printing, which demonstrated that it can be used for the reliability assessment of the screen printing process.