Scientists from Skoltech, together with their collaborators in the United States and Singapore, have developed a neural network that enables the tweaking of semiconductor crystals in a controlled way to achieve excellent properties for electronics.
This facilitates a new way of developing next-generation solar cells and chips by leveraging a controllable deformation that could potentially alter the properties of a material on the go. The study was published in the npj Computational Materials journal.
At the nanoscale level, materials are capable of resisting major deformation. In the so-called strained state, they show significant electronic, thermal, optical and other characteristics as a result of variations in the interatomic distances. The inherent properties of a strained material may vary, with the semiconducting silicon, for example, changing into a material that freely conducts the electric current.
By altering the strain level, it is possible to change the properties as required. This concept has led to a whole field of inquiry: elastic strain engineering (ESE). For example, this method can also be utilized to alter the performance of semiconductors, thereby offering a potential workaround for the impending Moore's law limit, when other options for increasing chip performance are exhausted.
One more potential use is in the field of solar cell development. According to Alexander Shapeev, a study co-author from Skoltech, a solar cell can be designed with tunable properties that can be altered on demand to optimize performance and adapt to external circumstances.
In earlier research, Skoltech PhD graduate Evgenii Tsymbalov, Associate Professor Alexander Shapeev, and their collaborators exploited ESE to convert nanoscale diamond needles from insulating to highly conductive and metal-like substances. Thus, they offered insights into the range of prospective applications of this technology. Currently, the team has come up with a convolutional neural network architecture that can guide ESE measures for semiconductors.
Evgenii Tsymbalov, Zhe Shi, Ming Dao, et al. Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass [open], npj Computational Materials (DOI: 10.1038/s41524-021-00538-0)