By Josh Perry, Editor
[email protected]

Researchers from the Massachusetts Institute of Technology (MIT) in Cambridge, Mass., the Skolkovo Institute of Science and Technology (Skoltech) in Moscow, Russia, and Nanyang technological University in Singapore discovered that applying strain to a semiconductor or crystalline material deforms the order of its atom and impacts its electrical, optical, and thermal properties.

Introducing a small amount of strain into crystalline materials, such as diamond or silicon, can produce significant changes in their properties, researchers have found.
(Chelsea Turner/MIT)

This research, according to a report from MIT, was based on previous work that discovered even a one percent change in the structure of silicon processors could enhance its speed by 50 percent and a study showing that diamond can be elastically stretched by as much as nine percent without failure when it is in nanometer needles.

While strain can be applied in six ways, in three different dimensions, and in countless gradations of degree, so it would be impossible to test all of the possibilities to determine what the resulting properties would be. So, the MIT researchers turned to machine learning.

Machine learning provided “a systematic way of exploring the possibilities and homing in on the appropriate amount and direction of strain to achieve a given set of properties for a particular purpose,” according to the report.

The report continued, “The team studied the effects of strain on the bandgap, a key electronic property of semiconductors, in both silicon and diamond. Using their neural network algorithm, they were able to predict with high accuracy how different amounts and orientations of strain would affect the bandgap.”

By tuning the bandgap, researchers could build more efficient devices that match the energy source that they are trying to capture. Strain engineering would also limit the unintended side effects that can result from chemically altering a material or using an electrical field to induce changes.

The research was recently published in the Proceedings of the National Academy of Sciences (PNAS). The abstract stated:

“Nanoscale specimens of semiconductor materials as diverse as silicon and diamond are now known to be deformable to large elastic strains without inelastic relaxation. These discoveries harbinger a new age of deep elastic strain engineering of the band structure and device performance of electronic materials.

“Many possibilities remain to be investigated as to what pure silicon can do as the most versatile electronic material and what an ultrawide bandgap material such as diamond, with many appealing functional figures of merit, can offer after overcoming its present commercial immaturity. Deep elastic strain engineering explores full six-dimensional space of admissible nonlinear elastic strain and its effects on physical properties.

“Here we present a general method that combines machine learning and ab initio calculations to guide strain engineering whereby material properties and performance could be designed. This method invokes recent advances in the field of artificial intelligence by utilizing a limited amount of ab initio data for the training of a surrogate model, predicting electronic bandgap within an accuracy of 8 meV.

“Our model is capable of discovering the indirect-to-direct bandgap transition and semiconductor-to-metal transition in silicon by scanning the entire strain space. It is also able to identify the most energy-efficient strain pathways that would transform diamond from an ultrawide-bandgap material to a smaller-bandgap semiconductor.

“A broad framework is presented to tailor any target figure of merit by recourse to deep elastic strain engineering and machine learning for a variety of applications in microelectronics, optoelectronics, photonics, and energy technologies.”