Spin- and Voltage-dependent emission from Intra- and Intermolecular TADF OLEDs

Organic light emitting diodes (OLEDs) based on thermally activated delayed fluorescence (TADF) utilize molecular systems with a small energy splitting between singlet and triplet states. This can either be realized in intramolecular charge transfer states of molecules with near-orthogonal donor and acceptor moieties or in intermolecular exciplex states formed between a suitable combination of individual donor and acceptor materials. Here, we investigate 4,4'-(9H,9'H-[3,3'-bicarbazole]-9,9'-diyl)bis(3-(trifluoromethyl) benzonitrile) (pCNBCzoCF3), which shows intramolecular TADF but can also form exciplex states in combination with 4,4',4''-tris[phenyl(m-tolyl)amino]triphenylamine (m-MTDATA). Orange emitting exciplex-based OLEDs additionally generate a sky-blue emission from the intramolecular emitter with an intensity that can be voltage-controlled. We apply electroluminescence detected magnetic resonance (ELDMR) to study the thermally activated spin-dependent triplet to singlet up-conversion in operating devices. Thereby, we can investigate intermediate excited states involved in OLED operation and derive the corresponding activation energy for both, intra- and intermolecular based TADF. Furthermore, we give a lower estimate for the extent of the triplet wavefunction to be >1.2 nm. Photoluminescence detected magnetic resonance (PLDMR) reveals the population of molecular triplets in optically excited thin films. Overall, our findings allow us to draw a comprehensive picture of the spin-dependent emission from intra- and intermolecular TADF OLEDs.Comment: 9 pages, 5 figure

Pfade angeregter Zustände in Organischen Leuchtdioden dritter Generation

This work revealed spin states that are involved in the light generation of organic light-emitting diodes (OLEDs) that are based on thermally activated delayed fluorescence (TADF). First, several donor:acceptor-based TADF systems forming exciplex states were investigated. Afterwards, a TADF emitter that shows intramolecular charge transfer states but also forms exciplex states with a proper donor molecule was studied. The primary experimental technique was electron paramagnetic resonance (EPR), in particular the advanced methods electroluminescence detected magnetic resonance (ELDMR), photoluminescence detected magnetic resonance (PLDMR) and electrically detected magnetic resonance (EDMR). Additional information was gathered from time-resolved and continuous wave photoluminescence measurements.In dieser Arbeit wurden Spinzustände identifiziert, die an der Lichterzeugung von organischen Leuchtdioden beteiligt sind, welche auf thermisch aktivierter verzögerter Fluoreszenz (engl. TADF) basieren. Zuerst wurden mehrere Donor:Akzeptor basierte TADF Systeme untersucht. Danach wurde ein TADF Emitter studiert, welcher intramolekulare Ladungstransfer Zustände (engl. CT states) zeigt, aber auch Exziplex Zustände mit einem geeigneten Donor Molekül bildet. In erster Linie wurde die experimentelle Methode der Elektronenspinresonanz (ESR) genutzt, insbesondere die erweiterten Techniken Elektrolumineszenz detektierte Magnetresonanz (ELDMR), Photolumineszenz detektierte Magnetresonanz (PLDMR) und elektrisch detektierte Magnetresonanz (EDMR). Zusätzliche Informationen wurden aus zeitaufgelösten und dauerstrich Photolumineszenz Messungen gewonnen

Training multilayer perceptrons by principal component analysis

We present a training algorithm for multilayer perceptrons which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix which is computed from the example inputs and their target outputs. For large networks the novel procedure requires far fewer examples for good generalization than traditional on-line algorithms.

Efficiently Learning Multilayer Perceptrons

A learning algorithm for multilayer perceptrons is presented which is based on finding the principal components of a correlation matrix computed from the example inputs and their target outputs. For large networks our procedure needs far fewer examples to achieve good generalization than traditional on-line algorithms.

Efficient training of multilayer perceptrons using principal component analysis

A training algorithm for multilayer perceptrons is discussed and studied in detail, which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix computed from the example inputs and their target outputs. Typical properties of the training procedure are investigated by means of a statistical physics analysis in models of learning regression and classification tasks. We demonstrate that the procedure requires by far fewer examples for good generalization than traditional online training. For networks with a large number of hidden units we derive the training prescription which achieves, within our model, the optimal generalization behavior.