Reviews on Machine Learning Approaches for Process Optimization in Noncontact Direct Ink Writing

Recently, machine learning has gained considerable attention in noncontact direct ink writing because of its novel process modeling and optimization techniques. Unlike conventional fabrication approaches, noncontact direct ink writing is an emerging 3D printing technology for directly fabricating low-cost and customized device applications. Despite possessing many advantages, the achieved electrical performance of produced microelectronics is still limited by the printing quality of the noncontact ink writing process. Therefore, there has been increasing interest in the machine learning for process optimization in the noncontact direct ink writing. Compared with traditional approaches, despite machine learning-based strategies having great potential for efficient process optimization, they are still limited to optimize a specific aspect of the printing process in the noncontact direct ink writing. Therefore, a systematic process optimization approach that integrates the advantages of state-of-the-art machine learning techniques is in demand to fully optimize the overall printing quality. In this paper, we systematically discuss the printing principles, key influencing factors, and main limitations of the noncontact direct ink writing technologies based on inkjet printing (IJP) and aerosol jet printing (AJP). The requirements for process optimization of the noncontact direct ink writing are classified into four main aspects. Then, traditional methods and the state-of-the-art machine learning-based strategies adopted in IJP and AJP for process optimization are reviewed and compared with pros and cons. Finally, to further develop a systematic machine learning approach for the process optimization, we highlight the major limitations, challenges, and future directions of the current machine learning applications.