​Link to join Webinar
https://kaust.zoom.us/j/99351224955

ABSTRACT: Advances in machine learning and artificial intelligence has been mainly from the architecture and software levels, with the basic building blocks still being CMOS transistors. This causes the hardware to be bulky and power-hungry. We are developing neuromorphic devices functioning similar to the neurons and synapses of the human brain, which can serve as the basic building blocks of neural network hardware, making the systems energy-efficient, scalable and compact. In this talk, Roy will discuss her use of novel semiconducting two-dimensional materials to make artificial neurons and synapses which can perform pattern recognition. She will also discuss her efforts in developing optoelectronic synapses using 2D materials.


BIOGRAPHY: Dr. Tania Roy is an Assistant Professor at NanoScience Technology Center and Department of Materials Science and Engineering at the University of Central Florida. Her current research interests lie in developing hardware for artificial intelligence applications using novel functional materials including two-dimensional materials. She also works on energy-harvesting devices, particularly advanced photon management systems in ultra-thin solar cells. She won the NSF CAREER award in 2019 and the UCF Luminary award in 2021. Prior to joining UCF in 2016, Roy was a postdoctoral researcher at University of California, Berkeley and Georgia Institute of Technology. She obtained her PhD and MS degree in Electrical Engineering from Vanderbilt University.

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• CONTACTMazen E. Mero

​Abstract: "The corrosion of buried metals affects geosystems that range from pipelines and nuclear waste disposal to reinforced concrete and archeology. Associated costs exceed 1 trillion dollars per year worldwide, yet current classification methods for soil corrosivity have limited predictive capacity. This study -triggered by the recent development of the Revised Soil Classification System RSCS- seeks to identify the critical soil and environment properties that can improve the prediction of buried metal corrosion.
The experimental studies conducted as part of this research recognize the inherently electro-chemo-transport coupled nature of buried metal corrosion and places emphasis on phenomena that have been inadequately captured in previous studies, such as the effect of soil texture and fines plasticity, partial saturation, moisture cycles, and conditions in Sabkha environments. The comprehensive experimental program involves detailed protocols for specimen preparation, advanced visualization (X-ray micro-CT), corrosion residual characterization (XRD), and detailed image analyses of extracted coupons. Experiments include both laboratory mixtures and a wide range of field specimens gathered throughout Saudi Arabia; furthermore, field observations expand soil assessment to native environmental conditions. Theoretical analyses based on mass conservation and electrochemical phenomena complement the experimental study.
Experimental and analytical results lead to new soil corrosivity assessment guidelines. Results show the relevance of the sediment pore fluid saturation, sediment texture, air and water connectivity, active corroding areas, the effect of environmental cycles on buried metal corrosion and evolving backfill contamination". 

Zoom link: https://kaust.zoom.us/j/97540847472

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• CONTACTGloria M. Castro Quintero

​Link to join Webinar
https://kaust.zoom.us/j/95447473634

ABSTRACT: One of the greatest challenges of modern society is related to energy consumption, dissipation and waste. A prominent example is that of integrated electronics, where power dissipation issues have become one of its greatest challenges. In this talk, I will discuss how to characterize energy dissipation in electronics, like heating in transistors based on 2D materials or in the conductive filaments of resistive random-access memories (RRAM), using spatially resolved thermometry. As the size of materials and devices shrinks to nanometer, atomic, or even quantum scale, it is more challenging to characterize their thermal properties reliably. Scanning thermal microscopy (SThM) is an emerging method to obtain local thermal information of electronic devices by controlling and monitoring probe–sample thermal exchange processes. Gaining thermal insights of our electronics is essential to design energy efficient circuits and understand and optimize ultra-dense data storage.

Figure 1. Thermal measurements of conductive filaments in RRAM memory devices.

BIOGRAPHY: Miguel Muñoz Rojo received his PhD (2015) in Condensed Matter Physics & Nanotechnology from the Spanish National Research Council (CSIC) and M.S./B.S. in Physics from the Autonomous University of Madrid. He obtained a JAE pre-doctoral Fellowship from CSIC to study during his PhD how the reduction of dimensionality affects the transport properties of organic and inorganic thermoelectric materials. During this period of time, he carried out scientific stays at the Rensselaer Polytechnic Institute (New York, USA), the University of Bordeaux (France) and the University of California Berkeley (USA). In 2012, he participated in the 62nd Lindau Nobel Laureate Meeting in Physics after qualifying in an international competition among young talent scientists. From 2016 to 2018, he became a postdoctoral researcher at Stanford University, studying two dimensional (2D) materials and devices based on them for thermal, electrical, and thermoelectric applications. From 2018 to 2021, he was a Tenure Track Assistant Professor at the University of Twente. He was successful in obtaining funding for his research in the field of thermal conversion and management processes with national and international partners. He is now a permanent researcher at the National Research Council of Spain (CSIC) working at the institute of Micro and Nanotechnology of Madrid (IMN) within the group FINDER. He leads the area of advanced thermal management within this group. His research focuses on thermal management, energy harvesting, nano- and micro-scale thermometry and thermal sensing.

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• CONTACTMazen E. Mero