Mario Lanza (He/Him)

Associate Professor (Strategic Hire), National University of Singapore
  • Singapore

About Mario Lanza

Mario Lanza  is an IEEE Fellow and Associate Professor of Materials Science and Engineering at the  National University of Singapore (NUS), since August 2024. He was headhunted under the highly selective “Strategic Hire” program of the university, and he is currently acting as Deputy Head of the Materials Science and Engineering Department. Before joining NUS, he got the PhD in Electronic Engineering in 2010 at the Autonomous University of  Barcelona, where he graduated with honors and won the extraordinary PhD prize. In 2010-2011 he was  NSFC postdoctoral fellow at Peking University, and in 2012-2013 he was  Marie Curie postdoctoral fellow at Stanford University. On September  2013 he joined Soochow University (in China), where he promoted until  the rank of Full Professor. Between October 2020 and July 2024 he was full-time Associate Professor at the King Abdullah University of Science  and Technology (in Saudi Arabia), where he became known for his work in nano-electronics. 

He has published over 250  research articles in top journals like Nature, Science and Nature  Electronics, many of them becoming highly cited, and he has been plenary,  keynote, tutorial and invited speaker in over 150 conferences. Many of his former students are now Professors in top universities, such as Peking University and Fudan University, among others. He is often consulted by leading semiconductor  companies and publishers. He serves in the board governors of the IEEE - Electron Devices Society, and is member of the technical and management committee of top conferences in the field of  electron devices, like IEDM, IRPS and IPFA. He is the founder of two startup companies: Web of Talents (2021) and Newmorphic (2026). He speaks fluently five  languages: English, Chinese, German, Spanish and Catalan.

Intro Content

Nature Electronics

Wafer-scale integration of 2D materials in high-density memristive crossbar arrays for artificial neural networks

Memristors made of multilayer hexagonal boron nitride exhibit multiple properties (such as ultra-low power consumption and multiple stable states) that make them ideal for the construction of crossbar arrays for artificial neural networks.

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Recent Comments

Jan 04, 2022

Very nice paper and prototype, congratulations for the excellent work ! As a constructive observation, the endurance plot could be improved by presenting one data point per cycle instead of one data point per decade (see also M. Lanza et al. ACS Nano 2021, 15, 11, 17214–17231, also available here: https://pubs.acs.org/doi/10.1021/acsnano.1c06980).

Feb 24, 2021

Congratulations, outstanding work in the field of 2D materials !

Dec 08, 2020

Wonderful work, congratulations !

Sep 22, 2020

That is a very attractive work because it goes towards wafer scale integration and shows variability information. Congratulations to the authors !

Aug 25, 2020

Excellent work, congratulations to the entire team !

Aug 25, 2020

This is a fantastic work because it really addresses the problem of 2D solid-state microelectronic devices and circuits, which is integration and device-to-device variability. Congratulations to all the authors.

Nov 12, 2019

Congratulations Chetan ! Very beautiful stuff, I feel very happy for you. Well deserved !

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