Polymer-based artificial synapses: Using protons and electrons to impart plasticity to semiconductors

Institute of Microengineering - Distinguished Lecture

Due to the covid-19 restrictions currently in place, the lecture will take place remotely by zoom only.

Zoom Live Stream: https://epfl.zoom.us/j/843927942

Observe the new time: Due to the time zone difference, the lecture willl take place at 17h15 Lausanne Time.


Abstract: Organic semiconductors have been traditionally developed for making low-cost and flexible transistors, solar cells and light-emitting diodes. In the last few years, emerging applications in health case and bioelectronics have been proposed. A particularly interesting class of materials in this application area takes advantage of mixed ionic and electronic conduction in certain semiconducting polymers. Indeed, the ability to transduce ionic fluxes into electrical currents is useful when interacting with living matter or bodily fluids. My presentation 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.
The brain can perform massively parallel information processing while consuming only ~1- 100 fJ per synaptic event. I will describe a novel electrochemical neuromorphic device that switches at record-low energy (<0.1 fJ projected, <10 pJ measured) and voltage (< 1mV, measured), displays >500 distinct, non-volatile conductance states within a ~1 V operating range. Furthermore, it achieves record classification accuracy when implemented in neural network simulations. Our organic neuromorphic device works by combining ionic (protonic) and electronic conduction and is essentially similar to a concentration battery. The main advantage of this device is that the barrier for state retention is decoupled from the barrier for changing states, allowing for the extremely low switching voltages while maintaining non-volatility. Our synapses display outstanding speed (<20 ns) and endurance achieving over 10⁹ switching events with very little degradation all the way to high temperature (up to 120°C). These properties, which are unheard of in the realm of organic semiconcuctors, are very promising in terms of the 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 neural networks that learn with blind updates.

Bio: Alberto Salleo is currently Full Professor of Materials Science and Department Chair at Stanford University. Alberto Salleo holds a Laurea degree in Chemistry from La Sapienza 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 Alto Research Center. In 2005 Salleo joined the Materials Science and Engineering Department at Stanford as an Assistant Professor in 2006. Salleo is a Principal Editor of MRS Communications since 2011.While at Stanford, Salleo 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 highest 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.