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SUMMARY:Polymer-based artificial synapses: Using protons and electrons to 
 impart plasticity to semiconductors
DTSTART:20201123T171500
DTEND:20201123T180000
DTSTAMP:20260406T144429Z
UID:aa2f3b075fe49ff7a372459efda9afeb34697330c768dd708ed863dd
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Dr. Alberto Salleo\,\nStanford University\nInstitute of 
 Microengineering - Distinguished Lecture\n\nDue to the covid-19 restrictio
 ns currently in place\, the lecture will take place remotely by zoom only.
 \n\nZoom Live Stream: https://epfl.zoom.us/j/843927942\n\nObserve the new 
 time: Due to the time zone difference\, the lecture willl take place at 17
 h15 Lausanne Time. \n\n\nAbstract: Organic semiconductors have been tradit
 ionally developed for making low-cost and flexible transistors\, solar cel
 ls and light-emitting diodes. In the last few years\, emerging application
 s in health case and bioelectronics have been proposed. A particularly int
 eresting class of materials in this application area takes advantage of mi
 xed ionic and electronic conduction in certain semiconducting polymers. In
 deed\, the ability to transduce ionic fluxes into electrical currents is u
 seful when interacting with living matter or bodily fluids. My presentatio
 n will first discuss the fundamental aspects of how mixed conduction works
  in polymeric materials and show some applications in biosensing. The bulk
  of my talk will focus on polymer-based artificial synapses.\nThe brain ca
 n perform massively parallel information processing while consuming only ~
 1- 100 fJ per synaptic event. I will describe a novel electrochemical neur
 omorphic device that switches at record-low energy (<0.1 fJ projected\, <1
 0 pJ measured) and voltage (< 1mV\, measured)\, displays >500 distinct\, n
 on-volatile conductance states within a ~1 V operating range. Furthermore\
 , it achieves record classification accuracy when implemented in neural ne
 twork simulations. Our organic neuromorphic device works by combining ioni
 c (protonic) and electronic conduction and is essentially similar to a con
 centration battery. The main advantage of this device is that the barrier 
 for state retention is decoupled from the barrier for changing states\, al
 lowing for the extremely low switching voltages while maintaining non-vola
 tility. Our synapses display outstanding speed (<20 ns) and endurance achi
 eving over 109 switching events with very little degradation all the way t
 o high temperature (up to 120°C). These properties\, which are unheard of
  in the realm of organic semiconcuctors\, are very promising in terms of t
 he ability to integrate with Si electronics to demonstrate online learning
  and inference. When connected to an appropriate access device our device 
 exhibits excellent linearity\, which is an important consideration for neu
 ral networks that learn with blind updates.\n\nBio: Alberto Salleo is curr
 ently Full Professor of Materials Science and Department Chair at Stanford
  University. Alberto Salleo holds a Laurea degree in Chemistry from La Sap
 ienza and graduated as a Fulbright Fellow with a PhD in Materials Science 
 from UC Berkeley in 2001. From 2001 to 2005 Salleo was first post-doctoral
  research fellow and successively member of research staff at Xerox Palo A
 lto Research Center. In 2005 Salleo joined the Materials Science and Engin
 eering Department at Stanford as an Assistant Professor in 2006. Salleo is
  a Principal Editor of MRS Communications since 2011.While at Stanford\, S
 alleo won the NSF Career Award\, the 3M Untenured Faculty Award\, the SPIE
  Early Career Award\, the Tau Beta Pi Excellence in Undergraduate Teaching
  Award\, and the Gores Award for Excellence in Teaching\, Stanford’s hig
 hest teaching award. He has been a Thomson Reuters Highly Cited Researcher
  since 2015\, recognizing that he ranks in the top 1% cited researchers in
  his field.
LOCATION:Online https://epfl.zoom.us/j/843927942 https://epfl.zoom.us/j/84
 3927942
STATUS:CONFIRMED
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