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.Comment: 26 pages, 5 figure

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.Comment: 30 pages, 8 figures, 1 tabl

1/ f Noise and Machine Intelligence in a Nonlinear Dopant Atom Network

Noise exists in nearly all physical systems ranging from simple electronic devices such as transistors to complex systems such as neural networks. To understand a system's behavior, it is vital to know the origin of the noise and its characteristics. Recently, it was shown that the nonlinear electronic properties of a disordered dopant atom network in silicon can be exploited for efficiently executing classification tasks through “material learning.” Here, we study the dopant network's intrinsic 1/f noise arising from Coulomb interactions, and its impact on the features that determine its computational abilities, viz., the nonlinearity and the signal‐to‐noise ratio (SNR), is investigated. The findings on optimal SNR and nonlinear transformation of data by this nonlinear network provide a guideline for the scaling of physical learning machines and shed light on neuroscience from a new perspective

Schottky-Contact Formation between Metal Electrodes and Molecularly Doped Disordered Organic Semiconductors

We study using three-dimensional kinetic Monte Carlo (KMC) simulations to what extent the formation of Schottky contacts between a metal electrode and a molecularly doped disordered organic semiconductor can be understood from the theory for crystalline inorganic semiconductors, adapted to include the effects of the localized nature of the states in which the charge carriers reside and the hopping transport in between these states. The thickness of the Schottky-contact depletion region is found to be significantly smaller than as expected when the energetical disorder is neglected. The presence of energetic disorder is also found to influence the voltage dependence of the width of the depletion regions near the contacts of single-layer double-Schottky-contact devices. The voltage drop over the two depletion regions and the remaining charge-neutral bulk layer is shown to be described successfully by a semianalytical model, based on an accurately parameterized bulk mobility function of the dopant concentration, energetic disorder, and the electric field. We furthermore find that the mobility in the depletion regions is drastically reduced. As a result, the depletion-region formation process can be ultraslow, with a characteristic time scale ranging from microseconds to beyond milliseconds.</p

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

Image-Force-Stabilized Interfacial Dipole Layer Impedes Charge Injection into Disordered Organic Semiconductors

We show using three-dimensional kinetic Monte Carlo simulations that the injection of charge carriers from a metallic electrode into a disordered organic semiconductor is under nominally Ohmic injection conditions strongly impeded by the short-range Coulomb interactions between the charge carriers in the image-force-stabilized interfacial dipole layer. In contrast, master equation and conventional one-dimensional drift-diffusion simulations underestimate these Coulomb interactions due to their mean-field approximation, and are found not to reveal the effect. The simulations predict a reduction of the current density in organic semiconductor devices when the nominal injection barrier is taken very small or even negative, consistent with recent experimental results [Kotadiya et al., Nat. Mater. 17, 329 (2018)]. However, whereas in that work a modification of the energetic disorder near the interface is assumed, we find that the effect is already obtained after including charge-charge interactions beyond a one-dimensional and mean-field approximation. </p