Challenges and Limitations in Implementation

This chapter thus provides a brief summary of the critical issues and constraints relating to the integration of machine learning (ML) in the production of hybrid nanodevices and the technical and business difficulties that surround the mainstreaming of the technology. One of the primary issues with ML application in nanodevice fabrication is that the data used are often low quality and limited in availability. Nanofabrication is a sophisticated process that is characterized by variations in characteristics of materials and conditions, which allow a huge volume of high-quality data to be collected for model training for ML. For effectiveness of procedures used in fabrication, minimum accuracy comes from noisy or inadequate data, which leads to wrong predictions. Additionally, the computational costs remain high for higher-level algorithms; more so, the deep learning algorithms demand significant computing power for the handling of simulations as well as large dataset. This is because by increasing the cost of the production process, it becomes difficult for research laboratories and manufacturing facilities to purchase these technologies for extensive use. Nevertheless, the future of ML in hybrid nanodevice fabrication remains possibly challenging but promising. Some of the current limitations can be handled using transfer learning, edge computing, and self-learning systems, among others. Moreover, due to improvements in nanotechnology and ML, data generation will be more effective as well as computational approaches, and integration with the existing environments will be less challenging.