prof.dr.ir. W.G. van der Wiel (Wilfred)

PUBLICATIONS

>125 publications, >7,500 citations
Web of Science: h-index 30 (November 2021)
Google Scholar:  h-index 37 (November 2021)
ORCID

Full list of publications

FEATURED PUBLICATIONS

A deep-learning approach to realizing functionality in nanoelectronic devices
Hans-Christian Ruiz Euler, Marcus N. Boon, Jochem T. Wildeboer, Bram van de Ven, Tao Chen, Hajo Broersma, Peter A. Bobbert and Wilfred G. van der Wiel 
Nature Nanotechnology 15, 992-998 (2020)

Electrochemical Detection of Tumor-Derived Extracellular Vesicles on Nanointerdigitated Electrodes 
D. G. Mathew, P. Beekman, S. G. Lemay, H. Zuilhof, S. le Gac, W. G. van der Wiel 
Nano Letters 20, 820-828 (2020)

Classification with a disordered dopant-atom network in silicon 
T. Chen, J. van Gelder, B. van de Ven, S. Amitonov, B. de Wilde, H.-C. Ruiz-Euler, 
H. J. Broersma, P. A. Bobbert, F. A. Zwanenburg, W. G. van der Wiel 
Nature 577, 341-345 (2020) 

Bottom-Up Single-Electron Transistors
K. S. Makarenko, Z. H. Liu, M. P. de Jong, F. A. Zwanenburg, J. Huskens,
W. G. van der Wiel
Adv. Mater. 29, 1702920 (2017)

Evolution of a Designless Nanoparticle Network into Reconfigurable Boolean Logic
S. K. Bose, C. P. Lawrence, Z. Liu, K. S. Makarenko, R. M. J. van Damme, H. J. Broersma and W. G. van der Wiel 
Nature Nanotechnology 10, 1048 (2015)

Tunable Doping of a Metal with Molecular Spins
T. Gang, M. D. Yilmaz, D. Ataç, S.K. Bose, E. Strambini, A. H. Velders, M. P. de Jong,
J. Huskens and W. G. van der Wiel 
Nature Nanotechnology 7, 232 (2012)

Electron transport through double quantum dots
W. G. van der Wiel, S. De Franceschi, J.M. Elzerman, T. Fujisawa, S. Tarucha and
L. P. Kouwenhoven,
Rev. Mod. Phys. 75, 1 (2003)

The Kondo Effect in the Unitary Limit 
W. G. van der Wiel, S. De Franceschi, T. Fujisawa, J. M. Elzerman, S. Tarucha and
L. P. Kouwenhoven,
Science 289, 2105 (2000)

PATENTS

4 patents

RESEARCH INTERESTS

BRAIN-INSPIRED NANO SYSTEMS FOR ENERGY-EFFICIENT COMPUTING

Digital computing has dramatically changed our society. Despite its undeniable success, its technical progress is slowing down and facing physical and economical limits. Moreover, the energy consumption of digital IT is rapidly increasing to an unsustainable level. In order to overcome the limitations of digital computing – particularly, but not exclusively for machine-learning tasks – our group has been exploring the intrinsic physical properties of disordered, nanoscale networks. We have shown that such nonlinear systems can be trained to perform logic operations and canonical machine-learning tasks with potentially very high energy efficiency and small footprint.

Natural and man-made information processing systems differ greatly. Evolution has resulted in living systems that utilize whatever physical properties are exploitable to enhance the fitness for survival. Nature thereby exploits the emergent properties and massive parallelism of highly interconnected networks of locally active components. Man-made computers, however, are based on circuits of functional units, following rigid design rules. In conventional (classical) computational paradigms, potentially exploitable physical processes to solve a problem, are possibly left out. In our research, we manipulate physical systems using the principle of Material Learning, to take full advantage of the computational power of nanomaterial networks.

We have shown that a designless network of gold nanoparticles can be evolved into Boolean logic gates [1]. Later we demonstrated that the above principle is generic and can be demonstrated in other material systems as well, at much higher temperatures. By exploiting the nonlinearity of a nanoscale network of boron dopants in silicon (Si:B networks), we can significantly facilitate (nonlinear) classification [2]. We map a limited number of input data to a new, high-dimensional feature space, in which the data become linearly separable. Using a convolutional neural network approach, it becomes possible to use our device for handwritten digit recognition.

We also show that our Si:B network can be well described by a deep neural network, which allows for applying standard machine learning techniques in finding functionality [3]. We argue that this approach can be helpful in optimizing complex (quantum) nanoelectronic devices in general.

[1] Nature Nanotechnology 10, 1048 (2015) 
[2] Nature 577, 341-345 (2020)
[3] Nature Nanotechnology (2020)

 

NANOELECTRONIC BIOSENSING

Spin-off company ECsens

Publications
Nano Letters 20, 820-828 (2020)

 

ACOUSTOELECTRONICS

The oscillating (piezo-)electric fields accompanying surface acoustic waves (SAWs) are able to transport charge carriers in semiconductor nanostructures, where the SAW wavelength can be of the same order as the device size [1]. In our research, we apply nano imprint lithography (NIL) to define high-frequency (> 1 GHz) interdigitated transducers (IDTs) to electrically excite SAWs in a piezoelectric substrate [2,3]. In particular, we demonstrate acoustic transport of photo-generated electron-hole pairs in GaAs/AlGaAs core/shell nanowires on top of a LiNbO₃ substrate [4]. The wavelength of the acoustic modulation is smaller than the nanowire length. This allows for transporting the electrons and holes in a spatially separated fashion along the nanowire with a well-defined acoustic velocity towards indium doped recombination centers, where light is detected. Spatially resolved photoluminescence measurements indicate a relative transport efficiency of ~60%. By depositing a piezoelectric layer [3], acoustro-electronic transport experiments in non-piezoelectronic semiconductors like silicon is in reach.

[1] Phys. Rev. Lett. 96, 136807 (2006)
[2] Nanotechnology 23 315303 (2012)
[3] Appl. Phys. Lett. 102, 013112 (2013)
[4] Nanotechnology 25, 135204 (2014)

Applied Physics Letters 116, 011601 (2020)
Journal of Applied Physics 127, 214901 (2020)
Journal of Physics D: Applied Physics 53, 335301 (2020)   

 

HYBRID INORGANIC-ORGANIC ELECTRONICS

Where conventional electronics makes use mainly of top-down fabrication technology, the introduction of molecular materials paves the way for bottom-up fabrication as well (self-assembly). Hybrid electronic devices benefit from the strong aspects of both fabrication methods. In this research line we take advantage of all these characteristics of hybrid electronic devices to carry out experiments in a largely unexplored area of fundamental and broad scientific interest.

The conduction mechanism in organic (molecular) materials often differs drastically from that in their inorganic (crystalline) counterparts, and still many aspects remain to be understood. A very important and critical issue is the understanding of electronic phenomena at the interface between inorganic and molecular materials, as they usually play a dominant role in the overall properties.

Nature Nanotechnology 7, 232 (2012)
Adv. Mater. 29, 1702920 (2017)
Angew. Chem. Int. Ed. 57, 11465 (2018)
Advanced Electronic Materials 5, 1900041 (2019)

 

COMPLEX-OXIDE ELECTRONICS

Appl. Phys. Lett. 103, 201603 (2013)
Phys. Rev. Lett. 118, 106401 (2017)
Phys. Rev. B 97, 245113 (2018)
Applied Physics Letters 116, 011601 (2020)
Journal of Applied Physics 127, 214901 (2020)
Journal of Physics D: Applied Physics 53, 335301 (2020)
Physical Review Letters 124, 017702 (2020) 
2020 IEEE 33rd International Conference on Microelectronic Test Structures (ICMTS)

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Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

 

• 191211208 - Internship EE
• 191211219 - Master Thesis Project
• 191211650 - Multi-Disciplinary Design Project
• 193400131 - Nano-Optics
• 193400141 - Nano-Electronics
• 193650999 - Masters Assignment
• 201600017 - Final Project Preparation
• 201600187 - Individual Project
• 201700174 - Internship & Job Orientation Project NT
• 201700394 - CS Nano Electronics
• 201900200 - Final Project EMSYS
• 201900223 - Capita Selecta Electrical Engineering
• 202001144 - Single Electron Transistor
• 202001162 - Bachelor Thesis EE
• 202001434 - Internship EMSYS
• 202200122 - MSc-Thesis Project

• 191211208 - Internship EE
• 191211219 - Master Thesis Project
• 191211650 - Multi-Disciplinary Design Project
• 193400131 - Nano-Optics
• 193400141 - Nano-Electronics
• 193409200 - Master Thesis: General Aspects
• 193650999 - Masters Assignment
• 201600017 - Final Project Preparation
• 201600187 - Individual Project
• 201700174 - Internship & Job Orientation Project NT
• 201700240 - Master Thesis: Scientific Aspects
• 201700380 - MSc final project RGA CHE/NT
• 201800344 - Master's Assignment: Physics Aspects
• 201800345 - Master's Assignment: General Aspects
• 201900200 - Final Project EMSYS
• 201900223 - Capita Selecta Electrical Engineering
• 202000715 - TN MOD12: Bachelor’s Assignment
• 202000716 - Bachelor Assignment
• 202001144 - Single Electron Transistor
• 202001162 - Bachelor Thesis EE
• 202001434 - Internship EMSYS

Organisations

• Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Nano Electronics (NE)
• Brains (Center for Brain Inspired Nano Systems) (brains-center)