A5077463090- Advanced Memory and Neural Computing
- Neural Networks and Reservoir Computing
- Advancements in Semiconductor Devices and Circuit Design
- Ferroelectric and Negative Capacitance Devices
- Semiconductor materials and devices
- Neuroscience and Neural Engineering
- Machine Learning and ELM
- Quantum and electron transport phenomena
- Photonic and Optical Devices
- Force Microscopy Techniques and Applications
- Neural Networks and Applications
- Electrochemical Analysis and Applications
- Origins and Evolution of Life
- Entrepreneurship Studies and Influences
- Neural dynamics and brain function
- Conducting polymers and applications
- Electron and X-Ray Spectroscopy Techniques
- Innovation and Socioeconomic Development
- Quantum Mechanics and Applications
- Biofield Effects and Biophysics
- Machine Learning in Materials Science
University of Twente
2020-2025
Nano Hydrophobics (United States)
2022
Chan Heart Rhythm Institute
2022
The University of Texas at Austin
2003
Abstract A sulfonated polyaniline (SPAN) organic electrochemical network device (OEND) is fabricated using a simple drop‐casting method on multiple Au electrodes for use in reservoir computing (RC). The SPAN has humidity‐dependent electrical properties. Under high humidity, the OEND exhibits mainly ionic conduction, including charging of an electric double layer and diffusion. nonlinearity hysteresis current–voltage characteristics progressively increase with increasing humidity. rich...
Abstract The rapidly growing computational demands of deep neural networks require novel hardware designs. Recently, tuneable nanoelectronic devices were developed based on hopping electrons through a network dopant atoms in silicon. These ‘dopant processing units’ (DNPUs) are highly energy-efficient and have potentially very high throughput. By adapting the control voltages applied to its electrodes, single DNPU can solve variety linearly non-separable classification problems. However,...
A slight modification of one axiom quantum theory changes a reversible into time asymmetric theory. Whereas the standard Hilbert space does not distinguish mathematically between states (in-states scattering theory) and observables (out-``states'' new associates to two different Hardy subspaces which are dense in same analytic lower upper complex energy plane, respectively. As consequence this dynamical equations (Schr\"{o}dinger or Heisenberg) integrate semigroup evolution. Extending...
We demonstrate single-charge occupation of ambipolar quantum dots in silicon via charge sensing. have fabricated dot (QD) devices a metal-oxide-semiconductor heterostructure comprising single-electron transistor next to single-hole transistor. Both QDs can be tuned simultaneously sense transitions the other. further detect few-electron and few-hole regimes our device by active
Inspired by the highly efficient information processing of brain, which is based on chemistry and physics biological tissue, any material system its physical properties could in principle be exploited for computation. However, it not always obvious how to use a system’s computational potential fullest. Here, we operate dopant network unit (DNPU) as tuneable extreme learning machine (ELM) combine principles artificial evolution ELM optimise performance non-linear classification benchmark...
Deep learning, a multi-layered neural network approach inspired by the brain, has revolutionized machine learning. One of its key enablers been backpropagation, an algorithm that computes gradient loss function with respect to weights and biases in model, combination use descent. However, implementation deep learning digital computers is intrinsically energy hungry, consumption becoming prohibitively high for many applications. This stimulated development specialized hardware, ranging from...
The rapidly growing computational demands of deep neural networks require novel hardware designs. Recently, tunable nanoelectronic devices were developed based on hopping electrons through a network dopant atoms in silicon. These "Dopant Network Processing Units" (DNPUs) are highly energy-efficient and have potentially very high throughput. By adapting the control voltages applied to its terminals, single DNPU can solve variety linearly non-separable classification problems. However, using...
Reservoir Computing In materio reservoir computing is a direct way for mimicking spatiotemporal dynamics as found in animal brains. article number 2102688, Hirofumi Tanaka, Wilfred G. van der Wiel, Takuya Matsumoto, and co-workers report sulfonated polyaniline organic electrochemical random network device fabricated by simple dropcasting. They demonstrate speech-recognition task, exploiting the rich time of system.
Alegre-Ibarra et al., (2023). brains-py, A framework to support research on energy-efficient unconventional hardware for machine learning. Journal of Open Source Software, 8(90), 5573, https://doi.org/10.21105/joss.05573