arXiv subjects

Peter A. Bobbert

Publications and source records attributed to Peter A. Bobbert.

8 recordsLinked to original sources

arXiv2026

Space-Charge-Driven Nonlinear Charge Transport in Silicon Reconfigurable Nonlinear-Processing Units

Reconfigurable nonlinear-processing units (RNPUs) are multi-terminal electronic devices that act as computational primitives, exploiting intrinsic nonlinear charge transport combined with electrostatic tunability. Silicon-based realizations provide a scalable and technologically relevant platform toward unconventional computing hardware, yet the physical origin of their room-temperature nonlinearity has remained unresolved. Here, we demonstrate room-temperature operation in both boron- and arsenic-doped silicon RNPUs and show, using temperature- and length-dependent measurements supported by TCAD simulations, that charge transport is governed by space-charge effects. Interface trap states strongly suppress the equilibrium carrier density, while the functional nonlinearity arises from the competition between injected carriers and ionized dopants. The resulting transport evolves from Ohmic to strongly nonlinear and space-charge-limited current regimes, as evidenced by voltage and length scaling. The opposite-polarity background doping is shown to control the onset and strength of the nonlinearity, producing behavior beyond the quadratic dependence of the classical Mott-Gurney law. Agreement between experiment and simulation supports that the spatial distribution of injected carriers and fixed charge governs the electric-field profile and device response. These results establish a space-charge-based framework for RNPUs that does not require disorder or hopping transport, and provide design guidelines for scalable, CMOS-compatible nonlinear computing hardware.

cond-mat.other↗

arXiv2025

Dipole-quadrupole coupling in triplet exciton-polaron quenching in a phosphorescent OLED emission layer

Improving the efficiency and stability of organic light-emitting diodes (OLEDs) will further expand their present success in display applications. Triplet exciton-polaron quenching (TPQ) is an important cause of limited efficiency and stability in modern phosphorescent OLEDs, where triplet excitons are the emitting species. Lack of understanding of the TPQ mechanism in these OLEDs impedes the development of more efficient and stable OLEDs. We investigate the TPQ mechanism for triplet excitons on a phosphorescent guest interacting with hole polarons on a host. Our quantum-chemical calculations show that at distances relevant for TPQ the Förster approximation for the TPQ rate fails and that dipole-quadrupole coupling is dominant. This resolves a discrepancy between estimates of the TPQ rate obtained from an OLED device study and from the overlap between the emission spectrum of the emitter and absorption spectrum of the charged host. Equivalently to the Förster radius for dipole-dipole TPQ, the dipole-quadrupole TPQ rate can be quantified by a dipole-quadrupole radius obtained from the overlap between the emission spectrum of the emitter and the quadrupolar absorption spectrum of the charged host. The findings of this work are expected to have a broad relevance and to be useful in developing phosphorescent emitter-host combinations with reduced TPQ.

physics.atm-clus↗

arXiv2025

Gradient descent in materia through homodyne gradient extraction

Deep learning, a multi-layered neural network approach inspired by the brain, has revolutionized machine learning. One of its key enablers has been backpropagation, an algorithm that computes the gradient of a loss function with respect to the weights and biases in the neural network model, in combination with its use in gradient descent. However, the implementation of deep learning in digital computers is intrinsically energy hungry, with energy consumption becoming prohibitively high for many applications. This has stimulated the development of specialized hardware, ranging from neuromorphic CMOS integrated circuits and integrated photonic tensor cores to unconventional, material-based computing system. The learning process in these material systems, realized, e.g., by artificial evolution, equilibrium propagation or surrogate modelling, is a complicated and time-consuming process. Here, we demonstrate a simple yet efficient and accurate gradient extraction method, based on the principle of homodyne detection, for performing gradient descent on a loss function directly in a physical system without the need of an analytical description. By perturbing the parameters that need to be optimized using sinusoidal waveforms with distinct frequencies, we effectively obtain the gradient information in a highly robust and scalable manner. We illustrate the method in dopant network processing units, but argue that it is applicable in a wide range of physical systems. Homodyne gradient extraction can in principle be fully implemented in materia, facilitating the development of autonomously learning material systems.

cs.NE↗

arXiv2023

Critical nonlinear aspects of hopping transport for reconfigurable logic in disordered dopant networks

Nonlinear behavior in the hopping transport of interacting charges enables reconfigurable logic in disordered dopant network devices, where voltages applied at control electrodes tune the relation between voltages applied at input electrodes and the current measured at an output electrode. From kinetic Monte Carlo simulations we analyze the critical nonlinear aspects of variable-range hopping transport for realizing Boolean logic gates in these devices on three levels. First, we quantify the occurrence of individual gates for random choices of control voltages. We find that linearly inseparable gates such as the XOR gate are less likely to occur than linearly separable gates such as the AND gate, despite the fact that the number of different regions in the multidimensional control voltage space for which AND or XOR gates occur is comparable. Second, we use principal component analysis to characterize the distribution of the output current vectors for the (00,10,01,11) logic input combinations in terms of eigenvectors and eigenvalues of the output covariance matrix. This allows a simple and direct comparison of the behavior of different simulated devices and a comparison to experimental devices. Third, we quantify the nonlinearity in the distribution of the output current vectors necessary for realizing Boolean functionality by introducing three nonlinearity indicators. The analysis provides a physical interpretation of the effects of changing the hopping distance and temperature and is used in a comparison with data generated by a deep neural network trained on a physical device.

cs.ET↗

arXiv2022

Light-tunable three-phase coexistence in mixed halide perovskites

Mixed iodine-bromine perovskites used in solar cells undergo below a critical temperature an intrinsic demixing into phases with different iodine-bromine compositions. In addition, under illumination they show nucleation of an iodine-rich phase. We predict from thermodynamic considerations that in mixed iodine-bromine perovskites like MAPb(I$_{1-x}$Br$_x$)$_3$ the interplay of these effects can lead to coexistence of a bromine-rich, iodine-rich, and nearly iodine-pure nucleated phase. This three-phase coexistence occurs in a region in the composition-temperature phase diagram near the critical point for intrinsic demixing. We investigate the hysteresis in the evolution of this coexistence when temperature or illumination intensity are cycled. Depending on the particular way the coexistence is established, nearly iodine-pure nuclei should form either in the iodine-rich phase only or both in the bromine-rich and iodine-rich phases. Experimental verification of this fundamentally novel type of light-tunable three-phase coexistence should be possible by a combination of absorption and photoluminescence experiments.

cond-mat.mtrl-sci↗

arXiv2021

Dopant Network Processing Units: Towards Efficient Neural-network Emulators with High-capacity Nanoelectronic Nodes

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 of 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, a single DNPU can solve a variety of linearly non-separable classification problems. However, using a single device has limitations due to the implicit single-node architecture. This paper presents a promising novel approach to neural information processing by introducing DNPUs as high-capacity neurons and moving from a single to a multi-neuron framework. By implementing and testing a small multi-DNPU classifier in hardware, we show that feed-forward DNPU networks improve the performance of a single DNPU from 77% to 94% test accuracy on a binary classification task with concentric classes on a plane. Furthermore, motivated by the integration of DNPUs with memristor arrays, we study the potential of using DNPUs in combination with linear layers. We show by simulation that a single-layer MNIST classifier with only 10 DNPUs achieves over 96% test accuracy. Our results pave the road towards hardware neural-network emulators that offer atomic-scale information processing with low latency and energy consumption.

cs.LG↗

arXiv2019

Quantitative Predictions of Photoelectron Spectra in Amorphous Molecular Solids from Multiscale Quasiparticle Embedding

We present a first-principles-based multiscale simulation framework for quantitative predictions of the high-energy part of the Ultraviolet Photoelectron Spectroscopy (UPS) spectra of amorphous molecular solids. The approach combines a deposition simulation, many-body Green's Function Theory, polarizable film-embedding, and multimode electron-vibrational coupling and provides a molecular-level view on the interactions and processes giving rise to spectral features. This insight helps bridging the current gap between experimental UPS and theoretical models as accurate analyses are hampered by the energetic disorder, surface-sensitivity of the measurement and the complexity of excitation processes. In particular this is relevant for the unambiguous determination the highest occupied molecular orbital energy (HOMO) of organic semiconductors, a key quantity for tailoring and engineering new opto-electronic devices. We demonstrate the capabilities of the simulation approach studying the spectrum of two isomers of 2-methyl-9,10-bis(naphthalen-2-yl)anthracene (MADN) as archetypical materials showing a clearly separated HOMO peak in experiment. The agreement with experiment is excellent, suggesting that our approach provides a route for determining the HOMO energy with an accuracy better than 0.1eV.

cond-mat.mtrl-sci↗

arXiv2017

Charge transport in nanoscale vertical organic semiconductor pillar devices

We report charge transport measurements in nanoscale vertical pillar structures incorporating ultrathin layers of the organic semiconductor poly(3-hexylthiophene)(P3HT). P3HT layers with thickness down to 5 nm are gently top-contacted using wedging transfer, yielding highly reproducible, robust nanoscale junctions carrying high current densities (up to $10^6$ A/m$^2$). Current-voltage data modeling demonstrates excellent hole injection. This work opens up the pathway towards nanoscale, ultrashort-channel organic transistors for high-frequency and high-current-density operation.

cond-mat.mes-hall↗