Cesar Clavero

What kind of scientist are you at Intermolecular^®, and what do you do? I have been with Intermolecular^®  for almost 10 years, and I have enjoyed my time here since joining. I had the opportunity to contribute to diverse projects, from optical coatings to memory devices. My role has mostly been related to physical characterization and simulations, including using Machine Learning to assist material discovery. As a manager of the Advanced Characterization group, I had the opportunity to develop new capabilities to elevate our R&D activities. Currently, my team works on developing technologies such as Augmented Reality and Neuromorphic computing that will change the world in the future.

What is your favorite part about being a scientist, and how did you get  interested in science? I guess I have always been curious about my surroundings. I asked myself how things work. My father was a science teacher at the elementary school I attended and managed the laboratory. It was fascinating to see the microscopes, flasks, electric circuits, etc. That probably increased my interest in science. As I became a scientist and started researching different aspects of materials science, I found it fascinating to understand the fundamental properties of matter and publish my work in papers and conferences. My favorite part of being a scientist is when I struggle to understand a phenomenon, and after applying a systematic approach, I can finally explain to others my findings. 

How does your work contribute to material innovation for the future of technology? At Intermolecular^®, we have developed an approach to material innovation that combines high throughput experimentation with guided analytics
to accelerate R&D. For many applications, the current challenge is also related to the integration of such materials at low dimensionality. We have developed test vehicles during the last years that allow evaluating the performance of materials in the nanoscale, leading to very relevant results in the fields of memory and planarization. Machine Learning has also greatly helped us manage high volumes of data to understand previously overwhelming datasets.