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Research Scientist, Laboratoire de Physique des Interfaces et des Couches Minces, Palaiseau, France
laurie.calvet@cnrs.fr
Dr. Laurie Calvet received a BS in Applied Physics from Columbia University (1995) and MS (1996), M.Phil. (1998) and a Ph.D. (2001) in Electrical Engineering from Yale University. Here thesis work explored electronic transport in silicon schottky barrier transistors. In 2001 she began a post-doc in Professor George Whitesides group in the Chemistry department at Harvard University, focusing on molecule electronics. In 2002, she joined Marc Kastner's group in the Physics department at MIT and explored electronic transport in chemically synthesized nanoparticles. After a move to France in 2004, she joined Professor Albert Fert's group at the CNRS-Thales laboratory and explored molecular spintronics. In 2006, she joined C2N (formerly IEF), as a post-doc to investigate molecular spintronics with functional oxide electrodes. In 2007 she was recruited into the French CNRS. Her current research explores how to realize energy efficient devices and architectures that are inspired from biological systems.
RESEARCH:
RESEARCH:
Biologically inspired devices and architectures for a green and sustainable future
BIOLOGICAL INSPIRED DEVICES AND ARCHITECTURES USING ORGANIC ELECTRONICS:
Bayesian inference and spiking neuron circuits
Organic electronics which promises low cost low carbon footprint devices has exploded in recent years with the demonstration of robust of printeable devices on flexible substrates. It's use in neuromorphic devices has also gained wide interest recently with impressive demonstrations using organic electrochemical transistors (OECTs). This research has mostly focused on the demonstration of biomimetic properties and their potential use as sensors or as memory elements (synapses) in neuromorphic implementations. My research in this area focuses on realizing neurons and full classification circuits in a completely organic technology. I explore the realization of organic spiking neurons in both traditional semiconductor organic transistors and using OECT technologies. The goal is to use these organic spiking neurons to realize a complete classification circuit. One circuit envisioned is based on Bayesian inference where using the unique properites of OECTs can be exploited. This idea was recently awarded funding in the ANR-PLDT bilateral call on AI and information can be found on the BAYOEN link at the top of this page. Our targeted application is to use thse circuits for personal healthcare to detect warn about disease before it can progress to advanced stages.
More about my funded projects:
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