Machine Learning Enabled Extreme Ultraviolet Diffraction-based Imaging and Characterization of Nanoscale Semiconductor Structures and Devices

Abstract

As the world around us becomes evermore technologically integrated and devices become smaller, faster, and more efficient, so too do our favorite gadgets become more complex. Inside each one of our devices exists a complicated, multi-dimensional maze of components and materials that ultimately work together to bring us the technological wonders of today. These crucial structures are made via a complex flow of lithographic processes in which layers of material are patterned and deposited, removed and polished, implanted and etched away at the scale of only a few nanometers. The precise manipulation and creation of nanoscale structures is what enables new computing powers, more accurate sensors, and faster microelectronics.

These new technologies only work as long as the underlaying structures are near-perfectly made and the complexity and extreme small size of these components provides significant challenges in their characterization and imaging. Recently, a new imaging and characterization techniques using extreme ultraviolet (EUV) light (with a wavelength in range ~10 - 120 nm) have emerged that has the potential to non-destructively image and characterize complex nanostructures both spatially in 3D and compositionally. These techniques, EUV lensless imaging and scatterometry, rely on the use of computer algorithms to process patterns of scattered EUV light from a structure or device, which are then used to reveal the final image of the object. These techniques show great promise for non-destructive imaging and characterization of nanostructures; however, we are only beginning to scratch the surface of their power for characterizing actual microelectronic device structures.

In this project, you will work closely with a diverse team of scientists and experts within imec’s advanced patterning department and materials characterization laboratory to help usher in a new era of EUV-based imaging of semiconductor materials. In your work, you will drive the development of machine-learning (ML) algorithms to enable and enhance lensless EUV imaging and scatterometry of technologically relevant semiconductor structures and devices. The algorithms you develop will push the boundaries of the state-of-the-art in EUV imaging and characterization, increasing resolution and enabling exotic paradigms such as buried-layer/depth-resolved imaging and chemical/elemental mapping. These algorithms will then be applied on real-world semiconductor structures to help enable a new paradigm in characterization and imaging that supports development of evermore advanced devices.

Who You Are:

• Master’s degree in engineering, physics, mathematics, computer science, or a related field.
• Motivated, inquisitive student with a passion for learning new concepts and advancing current knowledge bases.
• Prior experience with optics, imaging processing and light propagation are considered strong advantages
• Familiarity with core machine learning architectures (e.g., PyTorch, Tensorflow, sci-kit-learn, etc.) is a strong plus
• Demonstrated programming experience is required (Python, MatLab, or equivalent language)
• Excellent written and oral communication in English

What We Provide:

• A diverse, dynamic working environment that employs international experts in nearly every discipline in semiconductors
• An extensive training network covering both technical and non-technical skillsets
• Participation in national/international conferences and workshops
• Fully funded scholarship (with benefits)