Neuro-inspired computing architectures are one of the leading candidates to solve complex and large-scale associative learning problems for AI applications. The two key building blocks for neuromorphic computing are the neuron and the synapse, which form the distributed computing and memory units. In the NeurONN project, we are proposing a novel neuro-inspired computing architecture where information is encoded in the “phase” of coupled oscillating neurons or oscillatory neural networks (ONN). Specifically, VO2 metal-insulator transition (MIT) devices and 2D memristors will be developed as neurons and synapses for hardware implementations. We predict VO2 MIT devices are up to 250X more energy efficient than state of the art digital CMOS based oscillators, where 2D memristors are up to 330X more energy efficient than state of the art TiO2 memristors. Moreover, the predicted energy efficiency gain of ONN architecture vs state of the art spiking neural network (SNN) architecture is up to 40X. Thus, NeurONN will showcase a novel and alternative energy efficient neuromorphic computing paradigm based on energy efficient devices and architectures. Such ONN will demonstrate synchronization and coupling dynamics for establishing collective learning behavior, in addition to desirable characteristics such as scaling, ultra-low power computation, and high computing performance. NeurONN aims to develop the first-ever ONN hardware platform (targeting two demonstrators) and complete with an ONN design methodology toolbox covering aspects from ONN architecture design to algorithms in order to facilitate adoption, testing and experimentation of ONN demonstrator chips by all potential users to unleash the potential of ONN technology.

Heat management is a paramount challenge in many cutting edge technologies, including new GaN electronic technology, turbine thermal coatings, resistive memories, or thermoelectrics. Further progress requires the help of accurate modeling tools that can predict the performance of new complex materials integrated in these increasingly demanding novel devices. However, there is currently no general predictive approach to tackle the complex multiscale modeling of heat flow through such nano and micro-structured systems. The state of the art, our predictive approach “ShengBTE.org”, currently covers the electronic and atomistic scales, going directly from them to predict the macroscopic thermal conductivity of homogeneous bulk materials, but it does not tackle a mesoscopic structure. This project will extend this predictive approach into the mesoscale, enabling it to fully describe thermal transport from the electronic ab initio level, through the atomistic one, all the way into the mesoscopic structure level, within a single model. The project is a 6 partner effort with complementary fields of expertise, 3 academic and 3 from industry. The widened approach will be validated against an extensive range of test case scenarios, including carefully designed experimental measurements taken during the project. The project will deliver a professional multiscale software permitting, for the first time, the prediction of heat flux through complex structured materials of industrial interest. The performance of the modeling tool will be then demonstrated in an industrial setting, to design a new generation of substrates for power electronics based on innovating layered materials. This project is expected to have large impacts in a wide range of industrial applications, particularly in the rapidly evolving field of GaN based power electronics, and in all new technologies where thermal transport is a key issue.

Our project aims to fill the gap between flexible electronic technology and design by developing highly predictive, generic, open-source, design-oriented organic and oxide based TFT compact model libraries, to be integrated in commercial Electron Design Automation (EDA) environments for full large area low cost circuit design for novel applications. These model libraries will be released together with parameter extraction standard templates to assist in the fast transfer between initial prototype device measurements to full product design. Such a facility will open the opportunity for wide flexible electronics design

EXTMOS’ main objective is to create a materials model and the related user friendly code that will focus on charge transport in doped organic semiconductors. Its aims are (i) to reduce the time to market of (a) multilayer organic light emitting devices, OLEDs, with predictable efficiencies and long lifetimes (b) organic thin film transistors and circuits with fast operation. (ii) to reduce production costs of organic devices by enabling a fully solution processed technology. Development costs and times will be lowered by identifying dopants that provide good device performance, reducing the number of dopant molecules that need to be synthesized and the materials required for trial devices. (iii) to reduce design costs at circuit level through an integrated model linking molecular design to circuit operation. Screening imposes the following requirements from the model 1. An improved understanding of dopant/host interactions at the molecular level. Doping efficiencies need to be increased to give better conducting materials. For OLEDs, dopants should not absorb visible light that lowers output nor ultraviolet light that can cause degradation. 2. An ability to interpret experimental measurements used to identify the best dopants. 3. The possibility of designing dopants that are cheap and (photo)chemically robust and whose synthesis results in fewer unwanted impurities, and that are less prone to clustering. The EXTMOS model is at the discrete mesoscopic level with embedded microscopic electronic structure and molecular packing calculations. Modules at the continuum and circuit levels are an integral part of the model. It will be validated by measurements on single and multiple layer devices and circuits and exploited by 2 industrial end users and 2 software vendors. US input is provided by an advisory council of 3 groups whose expertise complements that of the partners.