Charge Transport in Pure and Mixed Phases in Organic Solar Cells

In organic solar cells continuous donor and acceptor networks are consid- ered necessary for charge extraction, whereas discontinuous neat phases and molecularly mixed donor–acceptor phases are generally regarded as detrimental. However, the impact of different levels of domain continuity, purity, and donor–acceptor mixing on charge transport remains only semi- quantitatively described. Here, cosublimed donor–acceptor mixtures, where the distance between the donor sites is varied in a controlled manner from homogeneously diluted donor sites to a continuous donor network are studied. Using transient measurements, spanning from sub-picoseconds to micro- seconds photogenerated charge motion is measured in complete photovoltaic devices, to show that even highly diluted donor sites (5.7%–10% molar) in a buckminsterfullerene matrix enable hole transport. Hopping between isolated donor sites can occur by long-range hole tunneling through several buckmin- sterfullerene molecules, over distances of up to ≈4 nm. Hence, these results question the relevance of “pristine” phases and whether a continuous interpen- etrating donor–acceptor network is the ideal morphology for charge transport

Tail state limited photocurrent collection of thick photoactive layers in organic solar cells

We analyse organic solar cells with four different photoactive blends exhibiting differing dependencies of short-circuit current upon photoactive layer thickness. These blends and devices are analysed by transient optoelectronic techniques of carrier kinetics and densities, air photoemission spectroscopy of material energetics, Kelvin probe measurements of work function, Mott-Schottky analyses of apparent doping density and by device modelling. We conclude that, for the device series studied, the photocurrent loss with thick active layers is primarily associated with the accumulation of photo-generated charge carriers in intra-bandgap tail states. This charge accumulation screens the device internal electrical field, preventing efficient charge collection. Purification of one studied donor polymer is observed to reduce tail state distribution and density and increase the maximal photoactive thickness for efficient operation. Our work suggests that selecting organic photoactive layers with a narrow distribution of tail states is a key requirement for the fabrication of efficient, high photocurrent, thick organic solar cells

Mechanisms for Enhanced State Retention and Stability in Redox-Gated Organic Neuromorphic Devices

Recent breakthroughs in artificial neural networks (ANNs) have spurred interest in efficient computational paradigms where the energy and time costs for training and inference are reduced. One promising contender for efficient ANN implementation is crossbar arrays of resistive memory elements that emulate the synaptic strength between neurons within the ANN. Organic nonvolatile redox memory has recently been demonstrated as a promising device for neuromorphic computing, offering a continuous range of linearly programmable resistance states and tunable electronic and electrochemical properties, opening a path toward massively parallel and energy efficient ANN implementation. However, one of the key issues with implementations relying on electrochemical gating of organic materials is the state-retention time and device stability. Here, revealed are the mechanisms leading to state loss and cycling instability in redox-gated neuromorphic devices: parasitic redox reactions and out-diffusion of reducing additives. The results of this study are used to design an encapsulation structure which shows an order of magnitude improvement in state retention and cycling stability for poly(3,4-ethylenedioxythiophene)/polyethyleneimine:poly(styrene sulfonate) devices by tuning the concentration of additives, implementing a solid-state electrolyte, and encapsulating devices in an inert environment. Finally, a comparison is made between programming range and state retention to optimize device operation

Photogenerated Charge Transport in Organic Electronic Materials: Experiments Confirmed by Simulations

The performance of organic optoelectronic devices, such as organic photovoltaic (OPV) cells, is to a large extent dictated by their ability to transport the photogenerated charge, with relevant processes spanning a wide temporal (fs-mu s) and spatial (1-100 nm) range. However, time-resolved techniques can access only a limited temporal window, and often contradict steady-state measurements. Here, commonly employed steady-state and time-resolved techniques are unified over an exceptionally wide temporal range (fs-mu s) in a consistent physical picture. Experimental evidence confirmed by numerical simulations shows that, although various techniques probe different time scales, they are mutually consistent as they probe the same physical mechanisms governing charge motion in disordered media-carrier hopping and thermalization in a disorder-broadened density of states (DOS). The generality of this framework is highlighted by time-resolved experimental data obtained on polymer:fullerene, polymer:polymer, and small-molecule blends with varying morphology, including recent experiments revealing that low donor content OPV devices operate by long-range hole tunneling between non-nearest-neighbor molecules. The importance of nonequilibrium processes in organic electronic materials is reviewed, with a particular focus on experimental data and understanding charge transport physics in terms of material DOS.Funding Agencies|Knut and Alice Wallenberg Foundation [KAW 2016.0494]</p

Organic electronics for neuromorphic computing

Neuromorphic computing could address the inherent limitations of conventional silicon technology in dedicated machine learning applications. Recent work on silicon-based asynchronous spiking neural networks and large crossbar-arrays of two-terminal memristive devices has led to the development of promising neuromorphic systems. However, delivering a compact and efficient parallel computing technology, such as artificial neural networks embedded in hardware, remains a significant challenge. Organic electronic materials offer an attractive alternative for such systems and could provide biocompatible and relatively inexpensive neuromorphic devices with low-energy switching and excellent tunability. Here, we review the development of organic neuromorphic devices. We consider different resistance switching mechanisms, which typically rely on electrochemical doping or charge trapping, and discuss the challenges the field faces in implementing low power neuromorphic computing, which include device downscaling, improving device speed, state retention and array compatibility. We highlight early demonstrations of device integration into arrays and finally consider future directions and potential applications of this technology

Optimized pulsed write schemes improve linearity and write speed for low-power organic neuromorphic devices

Neuromorphic devices are becoming increasingly appealing as efficient emulators of neural networks used to model real world problems. However, no hardware to date has demonstrated the necessary high accuracy and energy efficiency gain over CMOS in both (1) training via backpropagation and (2) in read via vector matrix multiplication. Such shortcomings are due to device non-idealities, particularly asymmetric conductance tuning in response to uniform voltage pulse inputs. Here, by formulating a general circuit model for capacitive ion-exchange neuromorphic devices, we show that asymmetric nonlinearity in organic electrochemical neuromorphic devices (ENODes) can be suppressed by an appropriately chosen write scheme. Simulations based upon our model suggest that a nonlinear write-selector could reduce the switching voltage and energy, enabling analog tuning via a continuous set of resistance states (100 states) with extremely low switching energy (∼170 fJ • μm-2). This work clarifies the pathway to neural algorithm accelerators capable of parallelism during both read and write operations

Redox transistors for neuromorphic computing

Efficiency bottlenecks inherent to conventional computing in executing neural algorithms have spurred the development of novel devices capable of 'in-memory' computing. Commonly known as 'memristors,' a variety of device concepts including conducting bridge, vacancy filament, phase change, and other types have been proposed as promising elements in artificial neural networks for executing inference and learning algorithms. In this article, we review the recent advances in memristor technology for neuromorphic computing and discuss strategies for addressing the most significant performance challenges, including nonlinearity, high read/write currents, and endurance. As an alternative to two-terminal memristors, we introduce the three-terminal electrochemical memory based on the redox transistor (RT), which uses a gate to tune the redox state of the channel. Decoupling the 'read' and 'write' operations using a third terminal and storage of information as a charge-compensated redox reaction in the bulk of the transistor enables high-density information storage. These properties enable low-energy operation without compromising analog performance and nonvolatility. We discuss the RT operating mechanisms using organic and inorganic materials, approaches for array integration, and prospects for achieving the device density and switching speeds necessary to make electrochemical memory competitive with established digital technology

Optimized pulsed write schemes improve linearity and write speed for low-power organic neuromorphic devices

Neuromorphic devices are becoming increasingly appealing as efficient emulators of neural networks used to model real world problems. However, no hardware to date has demonstrated the necessary high accuracy and energy efficiency gain over CMOS in both (1) training via backpropagation and (2) in read via vector matrix multiplication. Such shortcomings are due to device non-idealities, particularly asymmetric conductance tuning in response to uniform voltage pulse inputs. Here, by formulating a general circuit model for capacitive ion-exchange neuromorphic devices, we show that asymmetric nonlinearity in organic electrochemical neuromorphic devices (ENODes) can be suppressed by an appropriately chosen write scheme. Simulations based upon our model suggest that a nonlinear write-selector could reduce the switching voltage and energy, enabling analog tuning via a continuous set of resistance states (100 states) with extremely low switching energy (∼170 fJ • μm-2). This work clarifies the pathway to neural algorithm accelerators capable of parallelism during both read and write operations