Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with...

Area of Science:

• Materials Science
• Computational Chemistry
• Electronics Engineering

Background:

• The electronics industry faces challenges in the post-Moore's law era, necessitating new semiconductor materials and fabrication techniques.
• Traditional trial-and-error methods for material discovery and device optimization are time-consuming and inefficient.
• Computational approaches, enhanced by data-driven strategies, present a powerful alternative.

Purpose of the Study:

• To highlight the advances of data-driven computational frameworks in semiconductor discovery and device development.
• To elaborate on the application of these frameworks in exploring the materials design space.
• To discuss the prediction of semiconductor properties and optimization of device fabrication using these methods.

Main Methods:

• Review and synthesis of current data-driven computational frameworks.
• Analysis of applications in materials design space exploration.
• Examination of predictive models for semiconductor properties.
• Evaluation of optimization techniques for device fabrication.

Main Results:

• Data-driven computational frameworks significantly enhance the exploration of novel semiconductor materials.
• These methods improve the accuracy and speed of predicting key semiconductor properties.
• Optimization of device fabrication processes is accelerated through computational modeling.
• Successful examples demonstrate the potential for accelerated discovery and development.

Conclusions:

• Data-driven computational frameworks are crucial for advancing semiconductor research and development in the post-Moore's law era.
• Challenges remain in data integration, model interpretability, and experimental validation.
• Future opportunities lie in developing more sophisticated, integrated, and interpretable computational tools.