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Optics study may pave way for faster, energy efficient computers

North Carolina State University physicists have made a significant advance in gaining a deeper understanding of how light interacts with matter.

Washington, April 29 : North Carolina State University physicists have made a significant advance in gaining a deeper understanding of how light interacts with matter.

Professor Dr. David Aspnes and post-doctoral Research Associate Dr. Eric Adles hope that their study will eventually pave the way for faster computers that use less energy.

Explaining their research in a study paper published in the journal Physical Review B, the researchers revealed that their work explained how wavelengths of light are shortened upon interaction with materials.

Aspnes says that his team's study may be used to gain a better understanding of how materials bond to each other, such as silicon and next-generation insulating materials for integrated-circuit technologies.

He says that such an advance may aid researchers in selecting and processing materials that bond to silicon more uniformly, resulting in faster computers that utilize energy more efficiently.

The researcher further says that the research allows scientists and engineers to use non-linear-optical spectroscopy - which examines light reflected, absorbed or produced by a substance to determine its physical properties - to obtain more accurate information on a substance at the atomic scale.

Aspnes believes that that study may be used to get better data on the physical properties of the "interface", the one-atom-thick layer where two materials bond to each other.

According to him, the study's results provide a "key" that can be used to analyse spectroscopy data.

Although previous research had enabled scientists to collect such data on the interface, there existed no means of interpreting it correctly on the atomic scale.

The goal of the research was to "improve our understanding of how things work", says Aspnes.

The study, however, also provided tools to better analyse data, which may be beneficial for manufacturers and industry scientists in making better decisions about how best to move forward.

ANI