In this study, the effect of atmospheric hydrogen plasma treatment on the in-plane conductivity of solution-processed zinc oxide (ZnO) in various environments is reported. The hydrogen-plasma-treated and untreated ZnO films exhibited ohmic behavior with room-temperature in-plane conductivity in a vacuum. When the untreated ZnO film was exposed to a dry oxygen environment, the conductivity rapidly decreased, and an oscillating current was observed. In certain cases, the thin film reversibly ‘switched’ between the high- and low-conductivity states. In contrast, the conductivity of the hydrogen-plasma-treated ZnO film remained nearly constant under different ambient conditions. We infer that hydrogen acts as a shallow donor, increasing the carrier concentration and generating oxygen vacancies by eliminating the surface contamination layer. Hence, atmospheric hydrogen plasma treatment could play a crucial role in stabilizing the conductivity of ZnO films.

Evolution-in-Materio is a computational paradigm in which an algorithm reconfigures a material's properties to achieve a specific computational function. This article addresses the question of how successful and well performing Evolution-in-Materio processors can be designed through the selection of nanomaterials and an evolutionary algorithm for a target application. A physical model of a nanomaterial network is developed which allows for both randomness, and the possibility of Ohmic and non-Ohmic conduction, that are characteristic of such materials. These differing networks are then exploited by differential evolution, which optimises several configuration parameters (e.g., configuration voltages, weights, etc.), to solve different classification problems. We show that ideal nanomaterial choice depends upon problem complexity, with more complex problems being favoured by complex voltage dependence of conductivity and vice versa. Furthermore, we highlight how intrinsic nanomaterial electrical properties can be exploited by differing configuration parameters, clarifying the role and limitations of these techniques. These findings provide guidance for the rational design of nanomaterials and algorithms for future Evolution-in-Materio processors.

We report on the electrical behaviour of thin films of bovine brain microtubules (MTs). For samples in both their dried and hydrated states, the measured currents reveal a power law dependence on the applied DC voltage. We attribute this to the injection of space-charge from the metallic electrode(s). The MTs are thought to form a complex electrical network, which can be manipulated with an applied voltage. This feature has been exploited to undertake some experiments on the use of the MT mesh as a medium for computation. We show that it is possible to evolve MT films into binary classifiers following an evolution in materio approach. The accuracy of the system is, on average, similar to that of early carbon nanotube classifiers developed using the same methodology.

In order to realise the potential of organic photovoltaic devices (OPVs) to provide cheap, scalable access to renewable energy, it is necessary to improve their lifetime and cost of encapsulation. The aim of this work is to achieve these aims by blending the donor and acceptor with the commodity polymer, PMMA, to form a ternary blend device with enhanced lifetime. We find that ternary OPV devices prepared in this manner can have up to double the lifetime of the binary control devices to an extent that depends upon the PMMA morphology and the processing additives used. Further, we find that the initial performance of ternary OPVs may decrease (in the case of PTB7-based blends) or slightly increase (in the case of P3HT-based blends) when compared to their binary counterparts, which is hypothesised to be due to donor compatibility with the PMMA. These findings suggest that this approach can be employed in other OPV blend systems and give design rules to maximise the positive impact on device lifetime.

We report the development of highly efficient and stable solution-processed organic light-emitting transistors (OLETs) that combine a polymer heterostructure with the transparent high-k dielectric poly(vinylidenefluoride(0.62)-trifluoroethylene(0.31)-chlorotrifluoroethylene(0.7)) (P(VDF-TrFE-CTFE)). The polymer heterostructure comprises of poly[4-(4,4-dihexadecyl-4H-cyclopenta[1,2-b:5,4-b']-dithiophen-2-yl)-alt-[1,2,5]thiadiazolo[3,4-c]pyridine] (PCDTPT) and Super Yellow as charge-transporting and light-emitting layers, respectively. Device characterization shows that the use of P(VDF-TrFE-CTFE) leads to larger channel currents (approximate to 2 mA) and lower operating voltages (-35 V) than for previously reported polymer based OLETs. Furthermore, the combined transparency of the dielectric and gate electrode, results in efficient bottom emission with external quantum efficiency of approximate to 0.88% at a luminance L >= 2000 cd m(-2). Importantly, the resulting OLETs exhibit excellent shelf life and operational stability. The present work represents a significant step forward in the pursuit of all-solution-processed OLET technology for lighting and display applications.

Solution‐processed hybrid organic–inorganic perovskite semiconductors have demonstrated remarkable performance for both photovoltaic and light‐emitting‐diode applications in recent years, launching a new field of condensed matter physics. However, their use in other emerging optoelectronic applications, such as light‐emitting field‐effect transistors (LEFETs) has been surprisingly limited, wth only a few low‐performance devices reported. The development of hybrid LEFETs consisting of a solution‐processed self‐organized multiple‐quantum‐well lead iodide perovskite layer grown onto an electron‐transporting In2O3/ZnO heterojunction channel is reported. The multilayer transistors offer bifunctional characteristics, namely, transistor function with high electron mobility (>20 cm2 V−1 s−1) and a large current on/off ratio (>106), combined with near infrared light emission (λmax = 783 nm) and a promising external quantum efficiency (≈0.2% at 18 cd m−2). A further interesting feature of these hybrid LEFETs, in comparison to previously reported structures, is their highly uniform and stable emission characteristics, which make them attractive for smart‐pixel‐format display applications.

This chapter provides a review of the different concepts that have been proposed for the realisation of electronic memory structures based on electro-active organic materials. One option is to develop the organic equivalents of existing (e.g. silicon-based) random access memory devices. Resistive memories also offer an attractive technology by which devices can easily be scaled. However, the operational principles of these deceptively simple memory elements have yet to be firmly established. A major challenge is the development of molecular-based memories, i.e. exploiting individual molecules or small groups of molecules. An even more radical approach is to look beyond the von Neumann architecture, which forms basis of most computers, and to emulate the memory processes that are found in nature.

A photolithographic process for the fabrication of short channel solution-processed zinc oxide transistors (ZnO TFTs) was optimized. To avoid damage to the ZnO film by the photolithography, a bottom gate, bottom contact (BG-BC) device structure was adopted. A perhydropolysilazane (PHPS) precursor, thermally annealed and then treated in oxygen plasma, was used as the gate insulator on an aluminum (Al) gate electrode. Al source and drain electrodes, with a minimum channel length of 5 μm were successfully defined using photolithography. A mobility of 1.5×10−2 cm2/Vs, on/off ratio of 106 and good contact between the source and drain (S/D) and semiconductor were achieved for solution-processed ZnO TFTs having various channel lengths, showing no degradation of device properties. The relatively low mobility was attributed to increased roughness of the gate insulator resulting from development during the photolithographic process. These results suggest that short-channel solution-processed ZnO TFTs can be fabricated by adopting optimised photolithographic processes.

We report the development of low operating voltages in inorganic-organic hybrid light-emitting transistors (HLETs) based on a solution-processed ZrO x gate dielectric and a hybrid multilayer channel consisting of the heterojunction In2O3/ZnO and the organic polymer "Super Yellow" acting as n- and p-channel/emissive layers, respectively. Resulting HLETs operate at the lowest voltages reported to-date (<10 V) and combine high electron mobility (22 cm2/(V s)) with appreciable current on/off ratios (≈103) and an external quantum efficiency of 2 × 10-2% at 700 cd/m2. The charge injection, transport, and recombination mechanisms within this HLET architecture are discussed, and prospects for further performance enhancement are considered.

This paper focuses on a performance analysis of single-walled-carbon-nanotube / liquid crystal classifiers produced by evolution in materio. A new confidence measure is proposed in this paper. It is different from statistical tools commonly used to evaluate the performance of classifiers in that it is based on physical quantities extracted from the composite and related to its state. Using this measure, it is confirmed that in an un-trained state, ie: before being subjected to an algorithm-controlled evolution, the carbon-nanotube-based composites classify data at random. The training, or evolution, process brings these composites into a state where the classification is no longer random. Instead, the classifiers generalise well to unseen data and the classification accuracy remains stable across tests. The confidence measure associated with the resulting classifier's accuracy is relatively high at the classes' boundaries, which is consistent with the problem formulation.

This paper presents a series of experiments demonstrating the capacity of single-walled carbon-nanotube (SWCNT)/liquid crystal (LC) mixtures to be trained by evolutionary algorithms to act as classifiers on linear and nonlinear binary datasets. The training process is formulated as an optimisation problem with hardware in the loop. The liquid SWCNT/LC samples used here are un-configured and with nonlinear current-voltage relationship, thus presenting a potential for being evolved. The nature of the problem means that derivative-free stochastic search algorithms are required. Results presented here are based on differential evolution (DE) and particle swarm optimisation (PSO). Further investigations using DE, suggest that a SWCNT/LC material is capable of being reconfigured for different binary classification problems, corroborating previous research. In addition, it is able to retain a physical memory of each of the solutions to the problems it has been trained to solve.

Evolution-in-materio is a form of unconventional computing combining materials' training and evolutionary search algorithms. In previous work, a mixture of single-walled-carbon-nanotubes (SWCNTs) dispersed in a liquid crystal (LC) was trained so that its morphology and electrical properties were gradually changed to perform a computational task. Material-based computation is treated as an optimisation problem with a hybrid search space consisting of the voltages used for creating the electrical field and the material's infinitely possible SWCNT arrangements in LC. In this paper, we study solutions using synthetic data with a non-linear separating boundary. In addition, results for two real life datasets with partly merged classes are presented. The training process is based on a differential evolution (DE) algorithm, which subjects the SWCNT/LC material to repeated electrical charging, leading to progressive morphological and electric conductivity modifications. It is shown that the material configuration the DE algorithm converges to form a non-negligible part of the solution. Furthermore, the problem's complexity is relevant to the properties of the resulting "physical solver". The material structures created when training for a problem allow the retraining for a less complex one. The result is a doubly-trained material that keeps the memory of the original more complex problem. This is not the case for doubly-trained materials where initial training is for the less complex problem. The optimal electric field found by the DE algorithm is also a necessary solution component for the material's output to be interpreted as a computation.

This paper presents a series of experiments demonstrating the capacity of single-walled carbon-nanotube (SWCNT)/liquid crystal (LC) mixtures to be trained by evolutionary algorithms to act as classifiers on linear and nonlinear binary datasets. The training process is formulated as an optimisation problem with hardware in the loop. The liquid SWCNT/LC samples used here are un-configured and with nonlinear currentvoltage relationship, thus presenting a potential for being evolved. The nature of the problem means that derivative-free stochastic search algorithms are required. Results presented here are based on differential evolution (DE) and particle swarm optimisation (PSO). Further investigations using DE, suggest that a SWCNT/LC material is capable of being reconfigured for different binary classification problems, corroborating previous research. In addition, it is able to retain a physical memory of each of the solutions to the problems it has been trained to solve.

The prospects of using organic materials in electronics and optoelectronics applications have attracted scientists and technologists since the 1970s. This field has become known as molecular electronics. Some successes have already been achieved, for example, the liquid-crystal display, organic light-emitting displays, and photoreceptors in electrophotography. Other products such as organic photovoltaic devices, chemical sensors and plastic transistors are developing fast. There is also a keen interest in exploiting technologies at the molecular scale that might eventually replace silicon devices. This chapter provides some of the background physics and chemistry to the interdisciplinary subject of molecular electronics. A review of some of the possible application areas for organic materials is presented and some speculation is provided regarding future directions.

Nanosphere lithography, an inexpensive and high throughput technique capable of producing nanostructure (below 100 nm feature size) arrays, relies on the formation of a monolayer of self-assembled nanospheres, followed by custom-etching to produce nanometre size features on large-area substrates. A theoretical model underpinning the self-ordering process by centrifugation is proposed to describe the interplay between the spin speed and solution concentration. The model describes the deposition of a dense and uniform monolayer by the implicit contribution of gravity, centrifugal force and surface tension, which can be accounted for using only the spin speed and the solid/liquid volume ratio. We demonstrate that the spin recipe for the monolayer formation can be represented as a pathway on a 2D phase plane. The model accounts for the ratio of polystyrene nanospheres (300 nm), water, methanol and surfactant in the solution, crucial for large area uniform and periodic monolayer deposition. The monolayer is exploited to create arrays of nanoscale features using ‘short’ or ‘extended’ reactive ion etching to produce 30–60 nm (diameter) nanodots or 100–200 nm (diameter) nanoholes over the entire substrate, respectively. The nanostructures were subsequently utilized to create master stamps for nanoimprint lithography.

We report on the lifetime of unencapsulated organic photovoltaic diodes (OPVs) based on a ternary blend of poly(3-hexylthiophene) (P3HT), phenyl-C-61-butyric acid methyl ester (PCBM) and a soft insulating polymer, poly(methyl methacrylate) (PMMA) as compared to reference binary P3HT:PCBM OPVs. The performance of ternary devices was shown to decay more slowly than that of their binary counterparts to an extent that depends on the relative humidity (RH). The power conversion efficiency of ternary OPVs when stored in a low humidity environment (1% RH) decayed to 80% of their initial value after 200 h, almost double that of the reference binary OPVs. AFM measurements suggest that the PMMA forms pillars within the P3HT:PCBM matrix. It is proposed that the PMMA absorbs water in the active layer, and in doing so, slows the rate of deep trap formation that would otherwise lead to enhanced Shockley-Read-Hall recombination.

The potential of Evolution in Materio (EiM) for machine learning problems is explored here. This technique makes use of evolutionary algorithms (EAs) to influence the processing abilities of an un-configured physically rich medium, via exploitation of its physical properties. The EiM results reported are obtained using particle swarm optimisation (PSO) and differential evolution (DE) to exploit the complex voltage/current relationship of a mixture of single walled carbon nanotubes (SWCNTs) and liquid crystals (LCs). The computational problem considered is simple binary data classification. Results presented are consistent and reproducible. The evolutionary process based on EAs has the capacity to evolve the material to a state where data classification can be performed. Finally, it appears that through the use of smooth signal inputs, PSO produces classifiers out of the SWCNT/LC substrate which generalise better than those evolved with DE.

Evolution In Materio (EIM) is concerned with solving computational problems by exploiting the physical properties of materials. This paper presents the results of using a particle swarm optimisation (PSO) algorithm for evolving logic circuits in single-walled carbon nanotubes (SWCNT) based composites on a special custom made platform. The material used is a composite of SWCNT dispersed randomly in a polymer forming a complex conductive network. Following the EIM methodology the conductance of the material is manipulated for evolving threshold based logic circuits. The problem is formulated as a constrained, mixed integer optimisation problem. It is solved using PSO in conjunction with the shortest position value rule. The results showed that the conductive properties of SWCNT can be used to configure these materials to evolve multiple input/output logic circuits.

Evolution-in-materio uses evolutionary algorithms to exploit properties of materials to solve computational problems without requiring a detailed understanding of such properties. We show that using a purpose-built hardware platform called Mecobo, it is possible to solve computational problems by evolving voltages and signals applied to an electrode array covered with a carbon nanotube–polymer composite. We demonstrate for the first time that this methodology can be applied to function optimization and also to the tone discriminator problem (TDP). For function optimization, we evaluate the approach on a suite of optimization benchmarks and obtain results that in some cases come very close to the global optimum or are comparable with those obtained using well-known software-based evolutionary approach. We also obtain good results in comparison with prior work on the tone discriminator problem. In the case of the TDP we also investigated the relative merits of different mixtures of materials and organizations of electrode array.