NanoElectronics roadmap for Europe: From nanodevices and innovative materials to system integration

Abstract The NEREID project (“NanoElectronics Roadmap for Europe: Identification and Dissemination”) is dedicated to mapping the future of European Nanoelectronics. NEREID’s objective is to develop a medium and long term roadmap for the European nanoelectronics industry, starting from the needs of applications to address societal challenges and leveraging the strengths of the European eco-system. The roadmap will also identify promising novel nanoelectronic technologies, based on the advanced concepts developed by Research Centres and Universities, as well as identification of potential bottlenecks along the innovation (value) chain. Industry applications include Energy, Automotive, Medical/Life Science, Security, loT, Mobile Convergence and Digital Manufacturing. The NEREID roadmap covers Advanced Logic and Connectivity, Functional Diversification (Smart Sensors, Smart Energy and Energy for Autonomous Systems), Beyond-CMOS, Heterogeneous Integration and System Design as well as Equipment, Materials and Manufacturing Science. This article gives an overview of the roadmap’s structure and content.

[1]  Michael Schmuker,et al.  A neuromorphic network for generic multivariate data classification , 2014, Proceedings of the National Academy of Sciences.

[2]  R. Jansen,et al.  The spin-valve transistor: a preview and outlook , 2003 .

[3]  Kerry Vahala,et al.  Cavity opto-mechanics. , 2007, Optics express.

[4]  A. Cleland.  Foundations of nanomechanics , 2002 .

[5]  T. Skotnicki,et al.  An innovative heat harvesting technology (HEATec) for above-Seebeck performance , 2012, 2012 International Electron Devices Meeting.

[6]  Enrico Macii,et al.  A Top-Down Constraint-Driven Methodology for Smart System Design , 2014, IEEE Circuits and Systems Magazine.

[7]  Peng Zeng,et al.  Kinetic Energy Harvesting Using Piezoelectric and Electromagnetic Technologies—State of the Art , 2010, IEEE Transactions on Industrial Electronics.

[8]  Anders Eklund,et al.  Spin-Torque and Spin-Hall Nano-Oscillators , 2015, Proceedings of the IEEE.

[9]  Pritish Narayanan,et al.  Neuromorphic computing using non-volatile memory , 2017 .

[10]  D. Ingber,et al.  From 3D cell culture to organs-on-chips. , 2011, Trends in cell biology.

[11]  Damien Querlioz,et al.  Neuromorphic computing with nanoscale spintronic oscillators , 2017, Nature.

[12]  S. Sarma,et al.  Spintronics: Fundamentals and applications , 2004, cond-mat/0405528.

[13]  C. David Wright,et al.  On-chip phase-change photonic memory and computing , 2017, NanoScience + Engineering.

[14]  F. Balestra.  Nanoscale CMOS: Innovative Materials, Modeling and Characterization , 2010 .

[15]  P. Abgrall,et al.  Lab-on-chip technologies: making a microfluidic network and coupling it into a complete microsystem—a review , 2007 .

[16]  S. Datta,et al.  Electronic analog of the electro‐optic modulator , 1990 .

[17]  Subhas Chandra Mukhopadhyay,et al.  Wearable Sensors for Human Activity Monitoring: A Review , 2015, IEEE Sensors Journal.

[18]  S. Thorpe,et al.  Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains , 2008, PloS one.

[19]  Hendrik Rogier,et al.  Wireless Power Transmission: R&D Activities Within Europe , 2014, IEEE Transactions on Microwave Theory and Techniques.

[20]  Stuart A. Wolf,et al.  Spintronics : A Spin-Based Electronics Vision for the Future , 2009 .

[21]  D. Grundler,et al.  Magnonics: Spin Waves on the Nanoscale , 2009 .