SYnaptic MOlecular NEtworks for Bio-inspired Information Processing (SYMONE)

The SYMONE long-term vision is to build multi-scale bio-/neuro-inspired systems interfacing/connecting molecular-scale devices to macroscopic systems for unconventional information processing with scalable neuromorphic architectures. The SYMONE computational substrate is a memristive/synaptic network controlled by a multi-terminal structure of input/output ports and internal gates embedded in a classical digital CMOS environment. The SYMONE goal is the exploration of a multiscale platform connecting molecular-scale devices into networks for the development and testing of synaptic devices and scalable neuromorphic architectures, and for investigating materials and components with new functionalities. The generic breakthrough concerns proof-of-concept of unconventional information processing involving flow of information via short-range interactions through a network of non-linear elements: switches, memristors/synapses. These will require several breakthroughs concerning the functionality of reasonably complex networks of simple components, and the fabrication of networks of devices, including self-assembly and multi-scale interfacing/contacting between such networks. Memristive networks are expected to solve unconventional computational problems, e.g. solving maze problems and implementing dynamic multiplexers. The overall SYMONE objectives are to implement 2D memristic arrays and networks, establish multi-scale electrical connections, and to demonstrate bio-inspired functional behaviour in such systems. On the experimental side, SYMONE will work with lithographically defined NxN arrays of proven individual memristive elements (Nanoparticle Organic Memory FETs (NOMFETs), as well as self-assembled nanoparticle (NP) networks (NPSAN) with functionalised NPs. The theoretical aspects involve detailed physical and compact models for the network elements and networks, and schemes for elementary information processing with such networks. SYMONE combines the advantages of a bottom-up approach based on molecular-scale objects and of a top-down approach based on functional modeling at the circuit level. The electronic properties of the nano-objects can be reproducibly modulated by the versatility of chemical synthesis. Such a solution is thus expected to provide continual scaling of device dimensions, or new architectures of electronics, or potential low-cost technologies, or all this together. SYMONE implements the vision of robust fault-tolerant information processing at molecular scale interfaced to conventional CMOS computers. The molecular-scale devices will be characterized and configured via post-fabrication learning without prior knowledge of the detailed structure of the self-assembled molecular network. This vision is also one of the very few routes for molecular scale information technology that does not suffer, from the start, from the same type of limitations as the ultimate CMOS technology with regard to ultra-dense computing applications.


Göran Wendin (contact)

Professor vid Chalmers, Microtechnology and Nanoscience (MC2), Electronics Material and Systems Laboratory

Per Hyldgaard

Professor vid Chalmers, Microtechnology and Nanoscience (MC2), Electronics Material and Systems Laboratory

Zoran Konkoli

Docent vid Chalmers, Microtechnology and Nanoscience (MC2), Electronics Material and Systems Laboratory


Centre national de la recherche scientifique (CNRS)

Paris, France

Technische Universität Dresden

Dresden, Germany

The French Alternative Energies and Atomic Energy Commission (CEA)

Grenoble Cedex 09, France

The Hebrew University Of Jerusalem

Jerusalem, Israel

United Arab Emirates University

Al Ain, United Arab Emirate

University of Basel

Basel, Switzerland


European Commission (FP7)

Funding years 2012–2015

Related Areas of Advance and Infrastructure

Sustainable development

Driving Forces



On Information Processing with Networks of Nano-Scale Switching Elements

Scientific journal article - peer reviewed

A generic simulator for large networks of memristive elements

Scientific journal article - peer reviewed

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