Device Configuration Research Articles

Room-temperature-processed transparent hemispherical optoelectronic array for electronic eyes

Bio-inspired electronic eyes mimicking the geometry and function of natural light-sensing organs are highly attractive in next-generation miniaturized imaging systems. The processing of electronic eyes usually adopts complex and equipment-intensive techniques, involving vacuum-based materials growth, doping, deposition and patterning. Here, we report an electronic eye system with a fully transparent artificial retina by a simple solution-based method at room-temperature. The artificial retina contains a flexible 16 × 16 photodetector array, which composes of a ZnO nanoparticle-MoS2 nanosheet composite film as the light sensing unit and the patterned silver nanowires as conductive paths within 1 cm2. It shows a reliable ultra-violet response and high transparence across the visible and near-infrared range, and hence allows the artificial retina to perceive light from all directions without weakening the photo-response. Demonstrations of two kinds of electronic eye prototypes (concave and convex hemispheres) in a single device configuration show the possibility for double-sided imaging. This work provides a simple, rapid and high-throughput processing route for bio-inspired electronic eye that could be used for integration of various functional materials with different properties, which opens a new avenue for the multifunctional electronic eye devices.

Reconfigurable Logic-in-Memory Constructed Using an Organic Antiambipolar Transistor.

We demonstrate an electrically reconfigurable two-input logic-in-memory (LIM) using a dual-gate-type organic antiambipolar transistor (DG-OAAT). The attractive feature of this device is that a phthalocyanine-cored star-shaped polystyrene is used as a nano-floating gate, which enables the electrical switching of individual logic circuits and stores the circuit information by the nonvolatile memory effect. First, the DG-OAAT exhibited Λ-shaped transfer curves with hysteresis by sweeping the bottom-gate voltage. Programming and erasing operations enabled the reversible shift of the Λ-shaped transfer curves. Furthermore, the top-gate voltage effectively tuned the peak voltages of the transfer curves. Consequently, the combination of dual-gate and memory effects achieved electrically reconfigurable two-input LIM operations. Individual logic circuits (e.g., OR/NAND, XOR/NOR, and AND/XOR) were reconfigured by the corresponding programming and erasing operations without any variations in the input signals. Our device concept has the potential to fulfill an epoch-making organic integration circuit with a simple device configuration.

Mining Electric Shovel Working Device Configuration Synthesis and Performance Analysis

Mining electric shovels (MES) are one of the key pieces of equipment for mining, and their comprehensive performance plays an important role in mining efficiency. Based on the screw theory, this paper proposes a comprehensive configuration method for an MES working device and selects a new mining electric shovel working device with a larger excavation range, taking the working device as an example for dimensional optimization and simulation analysis. Firstly, based on the closed-loop vector equation, the position inverse solution of the mechanism is analyzed, and the correctness of the position equation is verified by the simulation and by numerical solutions. Then, the constraints of the mechanism are analyzed, and the numerical method and the position equation are combined to solve for the workspace of the mechanism. The dimensional parameters of the mechanism are optimized by genetic algorithms. The workspace of the optimized working device is increased by 13.4789%. Finally, the mining results of the two MES, the working devices, are simulated and verified by experiment. It is shown that the experimental results are basically consistent with the simulation results. The excavation quality difference of the two working devices are 2.02% and 2.20%, which verifies the correctness of the kinematics equation of the working device and the feasibility of the new working device.

An electrolyte-free electrochromic device using aluminum as counter electrode material

In this study, a novel all-solid-state electrochromic device (ECD) free of indium tin oxide (ITO), tungsten(VI) oxide (WO3), and Li+ or any other liquid/gel electrolytes was prepared by a cost-effective and industrially applicable spray coating technique. The device configuration consists of a pre-formed Al-coated glass substrate for use as a counter electrode in the fabrication of an ECD with a glass/Al/Alq3/PEDOT:PSS structure. The fabricated device (2.5 × 4.0 cm2) demonstrated a reversible color change between a light-blue (off) and dark-blue (on) state, with good and homogeneous contrast. The ECD started coloring at − 0.5 V and was completely colored at – 2.0 V, showing various tones of blue according to the applied voltages. The coloration efficiency was 174.62 cm2/C and the cycle stability was achieved at least 150 times.

A Multi-User Cost-Efficient Crowd-Assisted VR Content Delivery Solution in 5G-and-Beyond Heterogeneous Networks

The latest evolution of wireless communications enables user access rich Virtual Reality (VR) services via the Internet, including while on the move. However, providing a premium immersive experience for massive number of concurrent users with various device configurations is a significant challenge due to the ultra-high data rate and ultra-low delay requirements of live VR services. This paper introduces an innovative multi-user cost-efficient crowd-assisted delivery and computing (MEC-DC) framework, which leverages mobile edge computing and end-user resources to support high performance VR content delivery over 5G-and-beyond heterogeneous networks (5G-HetNets). The proposed MEC-DC framework is based on three main solutions. First is a novel buffer-nadir-based multicast (BNM) mechanism for VR transmissions over 5G-HetNets. BNM ensures smooth and synchronized user viewing experience by maximizing the average playback buffer-nadir of all participants with stochastic optimization. Second and third are practical distributed algorithms: the cost-efficient multicast-aware transcoding offloading (MATO) and crowd-assisted delivery algorithm (CAD) which optimize jointly multicast delivery and video transcoding. The algorithms optimality and complexity were investigated. The proposed MATO-CAD solution was evaluated with real datasets, trace-driven numerical simulations, and prototype-based experiments. The trace-driven experimental results showed how the proposed solution provides 18% throughput improvement, lowest delay and best playback freeze ratio in comparison with three other state-of-the-art solutions.

Hydrovoltaic energy from water droplets: Device configurations, mechanisms, and applications

AbstractWater, the source of life, contains immense energy and manifests in diverse forms. Hydrovoltaic technology enables the direct interaction between water and materials to generate electricity, a vital necessity for industry modernization. Due to the ubiquitous presence and easy availability of falling water, hydrovoltaic energy derived from water droplets has attracted considerable attention and shown great potential in raindrop energy harvesting. In this review, a comprehensive summary of the latest advancements in harvesting hydrovoltaic energy from water droplets is presented, with a focus on the configurations and underlying mechanisms of hydrovoltaic devices. Additionally, a brief discussion on the applications of droplet‐based hydrovoltaic devices is presented, along with future prospects for this energy‐harvesting technology.

Tunneling spectroscopy of field-induced superconductivity in molybdenum disulfide using top metal contacts

Field-induced superconductivity has been observed on the surfaces of various materials; however, the underlying mechanism of this two-dimensional superconductivity remains elusive. While tunneling spectroscopy measurements provide valuable insights into the microscopic nature of the superconducting state, there is a scarcity of tunneling spectroscopy measurements specifically focused on field-induced superconductivity when compared to transport measurements. In this study, we present a novel approach for tunneling spectroscopy using top metal contacts on field-induced superconducting MoS2. Our experimental findings, including the energy gap values, are consistent with those of a previous study conducted using a different device configuration. The observed energy-dependent density of states cannot be explained by the conventional BCS mechanism. We address the impact of inhomogeneity within the superconducting phase and discuss potential methods for its suppression. The proposed tunneling spectroscopy technique offers simplicity and ease of implementation, making it applicable for investigating other two-dimensional superconducting systems.

Optimization of bifacial Ge-incorporated Sb2Se3 thin-film solar cells by modeling Cu2O back buffer layer

We have investigated bifacial Ge-incorporated Sb2Se3 thin-film solar cells by modeling and simulating a back buffer layer including Cu2O to optimize the bifacial device performance. A Ge-incorporated Sb2Se3 absorber layer was grown using Vapor Transport Deposition to extract the input parameters for the simulation, followed by exploring the properties of the transparent back buffer layer. After selecting a Cu2O back buffer layer, we conducted the optimization study of bifacial Ge-doped Sb2Se3 devices by varying a set of devices and materials parameters. The optical bandgap is characterized as 1.23 eV for Ge-incorporated Sb2Se3 absorber and the absorption coefficient is 3 × 105 cm−1 at 600 nm. Using these input parameters, we simulated the device configuration of ZnO:Al/i-ZnO/CdS/Ge-Sb2Se3/back buffer layer/transparent conductive oxide. Firstly, we checked the impact of conduction and valence band offsets on device performance, indicating the valence band offset of the back buffer layer plays a significant role. In particular, zero or small positive valence band offset minimizes a Schottky hole barrier, which could reduce hole current clamping near the back buffer region. As for back buffer doping concentration, high back buffer doping concentration (1 × 1015 cm−3 or higher) is required to improve device performance even with zero valence band offset, whereas low absorber doping concentration (<1 × 1014 cm−3) enables higher efficiency due to the enlarged energy band bending. For bifacial device performance, a Cu2O back buffer layer is simulated by varying critical input parameters such as absorber thickness, doping concentration, and defect concentration near the back buffer interface. The best efficiency of front-side illumination is 19.7%, Voc (744.4 mV), Jsc (40.14 mA/cm2), and FF (66.1%) and that of the rear-side illumination is 13.0%, Voc (724.5 mV), Jsc (31.6 mA/cm2), and FF (56.7%). Consequently, the bifaciality factor is 66%.

Growth, studies of milled and irradiated crystalline samples of DBNT for macro-photonic and electro-mechano functionalities

Single crystals of organic type of NLO crystalline material of DBNT - 8, 9-Dimethoxybenzo[b]naphtho [2,3-d] thiophene are proceeded to be grown by slow evaporation procedure and milled to micro scale and irradiated of Co-60 source of 100 Gy, 500 Gy and 5000 Gy for better scope of classification of system of the monoclinic type of DBNT-pure, micro and irradiated ones. The hardness study specifies the reverse indentation size effect (RISE) with work hardening coefficient above two of all DBNT specimen leads to the micro-tribological workings for springs with proper elastic parameters; the transmittance of DBNT of 5 specimens are 321 nm, 323 nm, 341 nm, 351 nm, and 352 nm for macro, micro, 100 Gy, 500 Gy, 5000 Gy. The photonic utility of identity for 3.86 eV and is 3.8629 eV by the transmittance data. The Non Linear Optical - NLO component this of 1.9, 1.94, 1.95, 1.96, 1.99 times that of KDP from which phase matching provision is enabled the influx property for the DBNT specimens is in the order of microns the (110) and (111) indexing represent for display device configuration. The dielectric behaviour of DBNT shows that polarization properly enabled for all categories by electrical performance, the abnormal variation is due to the vacancies created in the molecule by irradiation.

Work function modulation and charge transport studies of 100 MeV Au7+ ions irradiated TiO2 films for optoelectronic devices

This work demonstrates the impact of 100 MeV Au7+ ion irradiation on the work function and charge-transport properties of TiO2 thin films. The sol-gel synthesized TiO2 films were irradiated by 100 MeV Au7+ ions with varying fluences from 1 × 1011 ion-cm−2 to 1 × 1013 ion-cm−2. The surface potential of these films is found to vary from 139 mV to 469 mV for pristine to ion-irradiated TiO2 thin films. This alternatively tuned the work function of the TiO2 films from −4.87 eV to −4.53 eV which may be due to the higher oxygen vacancies and surface defects created by ion irradiation. The optical transmittance and band gap of the TiO2 thin films are found to be increased after ion irradiation. Photoluminescence and Raman measurements illustrate that various irradiation-induced defect levels such as self-trapped states, oxygen vacancies, color centers, and deep-level emission defects have altered in TiO2 films after ion irradiation. Further, important electrical parameters such as the Schottky barrier height, sheet resistance, and ideality factor have been evaluated to understand the impact of ion irradiation on the charge carrier transport properties of TiO2 films by fabricating the diode in the device configuration FTO/TiO2/Ag.

Happiness and productivity as government device in telework at the pandemic time

This article delineates the configuration of the government device based on the discursive and non-discursive practices of positive organizational psychology and its discourse of happiness and its relationship with autonomy, care and productivity in the period of the Covid 19 pandemic, which brought about great changes in the world of work, a device emerged that produced a subject that in the interactions with others had to discipline itself to contain the contagion by Sars-Cov-2 and at the same time continue to be productive, this productivity would be managed from some knowledge such as those of positive organizational psychology, legal and institutional provisions, regulations and guidelines. These strategies of governmentality organized some forms of teleworking, some techniques, ascetic, exercises, workshops, management of well-being that aim to make the subject organize on himself a set of subjective practices to achieve tranquility, creativity and productivity.

Geometrical Optimization and Simulation of NPDA Device for Future Use in Retinal Implant

The focus of this research was to improve the device structure and electrode geometry of nanophotodiode array (NPDA) subretinal implant devices for retinal implants, with the aim to restore the sight of people who have lost their vision and visual acuity (VA) to better than blindness level. In light of the electronic device simulator, the authors present a design depicting the configuration of a high-efficiency NPDA device by incorporating organic nanomaterials. The present researchers’ simulated NPDA device embeds 3600 stimulating pixels (100 μm in diameter) dispersed over a 5.5-mm active radius area. By optimizing the NPDA device geometry, authors demonstrated that each pixel has the potential to produce the required electrical current and voltage for neuronal stimulation utilizing an irradiance of 12 mW/mm2. Here, the authors concentrated on increasing the efficiency of the device because the increase in efficiency will tend to result in more pixels (greater number of electrodes by reducing the electrode geometry) if the increase in a pixel increases the visual perception. Therefore, theoretically, the 100-μm Tin electrode can reinstate VA up to 20/80.This NPDA implant has the potential to reinforce vision to a level of VA that is superior to that of the vision loss level.

Improving Photovoltaic Performance of Hybrid Organic-Inorganic MAGeI3 Perovskite Solar Cells via Numerical Optimization of Carrier Transport Materials (HTLs/ETLs).

In this study, a hybrid organic-inorganic perovskite solar cell (PSC) based on methylammonium germanium triiodide (MAGeI3), which is composed of methylammonium (CH3NH3+) cations and germanium triiodide (GeI3-) anions, has been numerically studied using SCAPS-1d codes. An extensive investigation of various electron transport layers (ETLs) and hole transport layers (HTLs) was conducted to identify the most optimal device configuration. The FTO/ZnOS/MAGeI3/PEDOT-WO3 structure performed the highest efficiency of all combinations tested, with an impressive optimized efficiency of 15.84%. This configuration exhibited a Voc of 1.38 V, Jsc of 13.79 mA/cm2, and FF of 82.58%. J-V characteristics and external quantum efficiency (EQE) measurements indicate that this device offers superior performance, as it has reduced current leakage, improved electron and hole extraction characteristics, and reduced trap-assisted interfacial recombination. Optimum device performance was achieved at active layer thickness of 560 nm. These findings may also serve as a basis for developing lightweight and ultra-thin solar cells, in addition to improving overall efficiency. Furthermore, a comprehensive correlation study was conducted to evaluate the optimum thickness and doping level for both ZnOS-ETL and PEDOT-WO3-HTL. The photovoltaic performance parameters of the FTO/ZnOS/MAGeI3/PEDOT-WO3 structure were analyzed over a wide temperature range (275 K to 450 K). The structure exhibited stable performance at elevated operating temperatures up to 385 K, with only minimal degradation in PCE of approximately 0.42%. Our study underscores the promise of utilizing cost-effective and long-term stability materials like ZnOS and PEDOT-WO3 alongside the toxic-free MAGeI3 perovskite. This combination exhibits significant potential for eco-friendly PSC, paving the way for the development of highly efficient ultra-thin PSC.

High‐Throughput Computing Guided Low/High Index Optical Coupling Layer for Record‐Performance Semitransparent Organic Solar Cells

AbstractSemitransparent organic solar cells (ST‐OSCs) can be made in different colors, allowing light to pass through, and yet absorb enough visible and near‐infrared (NIR) light to generate electricity. However, it remains a challenge to achieve high performing ST‐OSCs over the two competing indexes of power conversion efficiency (PCE) and average visible transmittance (AVT). This work reports an effort to develop record‐performance ST‐OSCs using a low/high index optical coupling layer (OCL) and a 2D photonic‐structured antireflective (AR) coating. High‐throughput optical screening is used to improve the understanding of OCL structure−performance relationships and the predicting of NIR absorption enhancement for ST‐OSCs. The concurrent use of a low/high index Na3AlF6 (170 nm)/ZnS (110 nm) OCL, identified among about 200 thousand simulated device configurations and a 900 nm pitch‐sized 2D photonic‐structured AR coating, fabricated using nanoimprint lithography, enables the record‐performance ternary PM6:BTP‐eC9:L8‐BO‐based ST‐OSCs, achieving simultaneously a record‐high PCE of 15.2%, a high AVT of 32%, an impressive light utilization efficiency of 4.86%, and a favorable color‐rendering index of 82. The results of the ST‐OSCs demonstrated in this work provide an attractive option for a plethora of applications in self‐powered greenhouses and building‐integrated photovoltaic systems.

Bifunctional photothermal membrane for high-temperature interfacial solar steam generation and off-grid sterilization

High-temperature solar steam generation is a promising sustainable strategy to alleviate the global clean water shortage. However, it is challenging to scale up the high-temperature steam generation under ambient conditions for water purification and steam sterilization simultaneously. Herein, we develop a solar-powered steam generating system to produce high-temperature steam for interfacial solar steam evaporation and off-grid sterilization. A three-layered photothermal membrane made of polystyrene foam, bubble wrap, and reduced graphene oxide (rGO) coated cloth produces high-temperature steam that can be condensed to clean water as well as sterilize microbes efficiently. The hybrid device configuration and higher solar light-absorbing capability of rGO result in high temperature steam generation (∼90 °C) to sterilize microorganisms such as S.aureus, E.coli, and G.stearothermophilus. Such a bifunctional device with high temperature steam generation and solar-steam evaporation capability could be a promising development for sustainable clean water generation and sterilization in remote areas.

A multimodal dual-fusion entity extraction model for large and complex devices

In the context of large and complex devices with a multi-source heterogeneous data environment, the extraction of network device configuration related entity information from diverse modalities of the Internet of Things data is a crucial and fundamental step towards establishing a domain knowledge graph for global Zero Touch Provisioning of network devices. In this paper, we present a novel multimodal dual-fusion entity extraction model that serves as a foundation for an intelligent and efficient network device configuration process. Firstly, the multimodal data is encoded, followed by the ViLBERT pre-training model to obtain more feature information of entities for multimodal front-end fusion. Next, the attention weights of each modal feature are learned through a multilayer neural network classifier and probabilistic graphical model, facilitating multimodal back-end fusion and reducing information redundancy. Finally, entity recognition is accomplished by employing a cohesive memory module that extracts the essential parameters for device configuration. The simulation results demonstrate that the proposed model performs exceptionally well on the MSCOCO2017 public dataset and the SFZK-Dev data network device dataset, with F1 values of the comprehensive evaluation index of model quality at 94.65% and 96.94%, respectively, indicating high stability. Additionally, the link prediction metrics HITS@1 achieved accuracy levels of 58.21% and 68.04%.

The product quality inspection scheme based on software-defined edge intelligent controller in industrial internet of things

The Industrial Internet of Things (IIoT) enables the improvement of the productivity and intelligent level of factory. The procedure of product quality inspection has generally adopted machine intelligence algorithms instead of manual operation to improve efficiency. In this paper, we propose a product quality inspection system scheme based on software-defined edge intelligent controller (SD-EIC). By adopting the software definition and resource virtualization technologies, the hardware platform of SD-EIC is designed to support the real-time control tasks and non-real-time edge computing tasks at the same time. To this end, we propose the scheme and architecture of product quality inspection system based on SD-EIC. Multiple virtual controllers and virtual edge computing nodes are constructed on a set of SD-EIC hardware platform to realize the integrated deployment of the real-time control for terminal devices and the AI model reasoning of product defect recognition algorithm based on machine vision respectively. In addition, the management and control scheme of product quality inspection system based on industrial information model is proposed. By constructing the semantic-based digital twin information model of terminal device, the flexible adjustment and parameter configuration of terminal device are realized to meet the demands of flexible production and manufacturing. The proposed product quality inspection system solution can effectively improve the utilization of hardware resources and the efficiency of product quality inspection, and reduce the overall deployment cost of the system. It can flexibly adapt to product diversity and different industrial scenarios.

Recent Advances in Materials, Structures, and Applications of Flexible Photodetectors

AbstractFlexible photodetectors (FPDs), which provide excellent advantages such as great wearability, portability, even implantability, have attracted tremendous interest in wearable healthcare monitoring, bendable imaging sensors, portable optical communication, etc. Recently, organic and perovskite‐based photoactive materials have been considered promising candidates for FPDs due to their superior optoelectronic performance, appealing mechanical flexibility, low‐temperature solution processability, and cost‐effectiveness. In this review, the milestone progress of organic, perovskite, and organic‐perovskite hybrid‐based FPDs and their applications in artificial intelligence are summarized. First, a brief introduction of device configurations, working principles, and key parameters for FPDs are presented. Subsequently, functional materials in FPDs, especially flexible photoactive materials of organic, perovskite, and organic‐perovskite hybrids, are summarized and analyzed. More importantly, representative applications of FPDs are summarized, including wearable healthcare monitoring, imaging sensors, and optical communication. The outlook and challenges for the field are proposed at the end. The purpose of this review is to not only elaborate on the fundamental design principles of FPDs, but also serve as a roadmap for next‐generation artificial intelligence sensing technologies.

Ultrahigh and Tunable Negative Photoresponse in Organic‐Gated Carbon Nanotube Film Field‐Effect Transistors

AbstractNegative photoconductance (NPC) detectors have attracted continuous attention for constructing advanced and novel optoelectronic devices, including reconfigurable image sensors and optosynaptic systems, especially by combining NPC with positive photoconductance (PPC). However, NPC devices suffer from much lower photosensitivity, slower response speed, and poor stability, especially in the infrared range. In this work, controllable NPC detectors based on organic‐gated carbon nanotube field‐effect transistors (OG‐CNT FETs) are reported and the strong influence of light‐induced electrostatic doping on the nonconventional photoresponse is demonstrated. The PM6/Y6‐based heterojunction allows efficient near‐infrared light absorption and facilitates exciton diffusion. By introducing a floating gate structure with an ultrathin dielectric layer, the OG‐CNT FET shows an enhanced NPC effect owing to in situ signal amplification. Compared to other device configurations, the optimal OG‐CNT FETs exhibit high responsivity of 72.6 A W−1 at 880 nm, along with improved response/recovery times of 7 and 5 ms. Impressively, gate‐tunable switching between NPC and PPC is observed under the same light illumination. The reversible switching can be attributed to the competition between the light‐controlled electrostatic coupling and the PM6/Y6 photovoltaic effect, which offers a new approach to achieve bidirectional photoresponses and paves the way for the development of future multifunctional optoelectronic systems.

A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint.

The performance and reliability of semiconductor devices scaled down to the sub-nanometer regime are being seriously affected by process-induced variability. To properly assess the impact of the different sources of fluctuations, such as line edge roughness (LER), statistical analyses involving large samples of device configurations are needed. The computational cost of such studies can be very high if 3D advanced simulation tools (TCAD) that include quantum effects are used. In this work, we present a machine learning approach to model the impact of LER on two gate-all-around nanowire FETs that is able to dramatically decrease the computational effort, thus reducing the carbon footprint of the study, while obtaining great accuracy. Finally, we demonstrate that transfer learning techniques can decrease the computing cost even further, being the carbon footprint of the study just 0.18 g of CO2 (whereas a single device TCAD study can produce up to 2.6 kg of CO2), while obtaining coefficient of determination values larger than 0.985 when using only a 10% of the input samples.