Layer Transfer Research Articles

Structural and electrochemical properties of undoped and In3+-doped multi-phase zinc- antimony oxide for a high-performance pseudocapacitor

Pseudocapacitive charge storage devices based on undoped and In3+-doped multi-phase zinc- antimony oxide (ZAO) with high potential insertion/removal processes of disulfide ions the electrodes were performed. The thin film morphologies related to a more regular grain shape, leading to enhanced surface mobility and nucleation density. The sizes of the nanoparticles (NPs) for undoped and In3+-doped ZAO were uniformly distributed between ~ 100 − 500 nm. Based on electronic structure calculations, the In3+-doped ZnSb2O4 behaves as a weak p-type semiconductor with a lower-than-band gap optical absorption and has greater charge storage performance diffused into the surface of electrode. This results in it obtaining a lower barrier layer and charge transfer resistance at the interface. In3+ doping in the ZAO electrode was driven in increments of energy density (77.34 Wh kg−1), power density (13.92 kW kg−1), specific capacitance (470 mAh g−1@10 mV s−1), and average capacitance retention (~87%). It is noted that the synthesized material in the present study and the importance of In3+ doping should be further considered for high-performance pseudocapacitor applications.

Improved Tracking of the Rheological Properties of Max-Bridge Oil-Based Mud Using Artificial Neural Networks.

Lab measurements for the rheological properties of mud are critical for monitoring the drilling fluid functions during the drilling operations. However, these measurements take a long time and might need more than one person to be completed. The main objectives of this research are to implement artificial intelligence for predicting the mud rheology from only Marsh funnel (μf) and measuring mud density (ρm) easily and quickly on the rig site. For the first time, an artificial neural network (ANN) was used to build different models for predicting the rheological properties of Max-bridge oil-based mud. The properties included the plastic viscosity (μp), yield point (γ), flow behavior index (η), and apparent viscosity (μa). Field measurements of 383 samples were used to build and optimize the ANN models. The obtained results showed that 32 neurons in the hidden layer and tan sigmoid function transfer function were the best parameters for all ANN models. The training and testing processes of models showed a strong prediction performance with a correlation coefficient (R) greater than 0.91 and an average absolute percentage error (AAPE) less than 5.31%. New empirical correlations were developed based on the optimized weights and biases of the ANN models. The developed empirical correlations were compared with the published correlations, and the comparison results confirmed that the ANN-developed correlations outperformed all previous work.

Annealing‐Free, High‐Performance Perovskite Solar Cells by Controlling Crystallization via Guanidinium Cation Doping

One of the urgent key points to realize the commercialization of perovskite solar cells (PSCs) with robust and excellent performance is the fabrication of high‐quality perovskite film. Nevertheless, a traditional thermal annealing (TA) technology is always necessary for a high crystallization perovskite film, and previous reports have suggested that TA could induce heterogeneous nucleation which is inconducive for the formation of smooth and uniform perovskite film, as well as time and cost consuming. Herein, an approach for the annealing‐free high‐quality perovskite film via the introduction of guanidinium iodine (GAI) is proposed. The organic molecule guanidinium (GA+) has a large ionic radius, and this could control the crystallizing rate of annealing‐free perovskite film. Ultimately, a perovskite film with larger grain size and lower defect density is acquired through doping 0.10 mol mL−1 GAI in the precursor solution. Moreover, the fabrication of the electron transfer layer and hole transfer layer is further realized at room temperature. Thus, all room temperature, annealing‐free high‐performance PSCs are demonstrated. Notably, a GAI‐doped device with an outstanding power conversion efficiency (PCE) of 19.25% is obtained, much higher than 16.78% of the pristine device.

Resilient moduli of demolition wastes in geothermal pavements: Experimental testing and ANFIS modelling

Construction and demolition (C&D) waste materials have been used in a wide range of civil engineering applications, particularly as unbound pavement materials. A comprehensive understanding of the deformation and thermal properties of C&D materials is necessary for their usage in novel applications related to heat transfer in pavement unbound layers, such as geothermal pavements. This research study focused on developing a correlation between the resilient modulus (MR) and thermal conductivity of C&D materials for geothermal pavement applications. The thermal conductivity of C&D materials, namely recycled concrete aggregate (RCA), crushed brick (CB), reclaimed asphalt pavement (RAP), and waste rock (WR), was evaluated at different moisture contents and dry densities. The MR and permanent deformation responses of C&D materials were characterized at the optimum moisture content (OMC), 85%OMC, and 70%OMC, using the repeated load triaxial (RLT) test. An intelligent model was developed for predicting the MR of C&D materials incorporating thermal conductivity, physical properties, confining stress, and deviator stress as input parameters using adaptive neuro-fuzzy inference system (ANFIS) approach. The developed ANFIS model had excellent performance in predicting the MR of C&D materials, with R2 = 0.99 for both training and testing datasets. The ANFIS model was converted into a mathematical relationship, which can be used by researchers and practitioners for estimating the MR of C&D materials.

Using Artificial Neural Networks to Predict Influences of Heterogeneity on Rock Strength at Different Strain Rates.

Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.

(Invited) Layer Transfer Technology for Stacked Multi-Channel Semiconductor-on-Insulator Platform

Continuous scaling of device dimension pushed Si channel to its physical limit, alternative high mobility channels such as (Si)Ge and (In)GaAs have been considered to replace Si to further enhance CMOS performance. In the meantime, ultrathin body (UTB) semiconductor-on-insulator is compulsory structure for improved gate control and immunity against short channel effects in the ultimately scaled high performance and low power dissipation CMOS. To realize stacked multi- channel semiconductor-on-insulator platform on current mainstream Si wafers, layer transfer technology [1-3] utilizing direct bonding and selective etching was developed to integrate (Si)Ge or (In)GaAs channels on Si, showing great potential for ultimate device structures with heterogeneous integration.Figure 1(a) is the process flow for integrating InGaAs channel onto SiGe-on-insulator (SGOI) structure utilizing layer transfer technology [4]. It started with bottom SGOI pMOSFET fabrication. The SGOI structure was realized by 2-step Ge condensation technique, resulting in Ge content of 70%. After high k metal gate process, self-aligned Ni-SiGe alloy was formed as S/D metal. Subsequently, SiO2 interlayer was deposited by CVD, followed by CMP planarization. Then, InGaAs grown on InP was directly bonded onto SiO2 and InP substrates were selectively etched away. Finally, the top InGaAsOI nMOSFET fabrication and metal interconnection completed the CMOS fabrication. Fig. 1(b) shows the cross-sectional TEM images of fabricated InGaAs/SiGe channel stacked CMOS. Well aligned top and bottom devices are seen without any distinct defects.Figures 2 is a schematic flow for fabricating Ge/Si double channel-on-insulator structure by low temperature hetero-layer bonding technology (LT-HBT). The technology starts with the preparation of donor wafers (Fig. 2(a)) for the top Ge channel layer and host wafers (Fig. 2(b)) for the bottom Si channel layer. For donor wafers, a 200-nm-thick Ge was epitaxially grown on SOI wafer by reduced pressure CVD. After CMP planarization, ALD SiO2 is deposited onto Ge. For host wafers, implantation and dopant activation were carried out, followed by chemical treatment for Si oxidation. After surface activation by room temperature chemical treatment (Fig. 2(c)), the direct wafer bonding of both SiO2 surface was performed at 200 °C in a press machine (Fig. 2(d)). Then, the Si substrate of the donor wafer was removed with deep RIE. At this time, the BOX layer of the donor wafer serves as an etch-stop layer. Subsequently, the BOX layer and thin Si layer of the donor wafer were removed selectively in HF and TMAH solution, respectively (Fig. 2(f)). To precise control the top Ge channel thickness, a series of low damage etching process was adapted to thin down Ge (Fig. 2(g)). The realized double channel structure is fully isolated with an insulating insertion layer,[5] which is very promising for realizing novel complementary FET (CFET) with minimum process steps. The cross-sectional TEM image of Ge/Si stacked channel of CFET after high k metal gate process is demonstrated in Fig. 2(h). Even more multiple stacks can be realized by using LT-HBT. A vertically stacked three-layer structure with 2 bottom Si layers and 1 top Ge layers is also demonstrated using SOI-based donor wafers in Fig. 3, indicating the highly integrated density channel structure is possible. The feasibility of layer transfer technology has been proved for multi-channel structure with high mobility channels for N2 node and beyond. Acknowledgement This work was supported by JST Japan-Taiwan Collaborative Research Program, Grant Number JPMJKB1902, Japan and the Ministry of Science and Technology under Grant Number MOST-109-2628-E-492-001-MY3, 109-2923-E-492-001-MY3, 107-2221-E-492-016-MY3.

III-V-on-Si transistor technologies: Performance boosters and integration

In this work, we review progress in III-V transistor technologies. Key approaches for silicon integration are described, with a distinction being made between large area layer transfer and selective growth techniques. We show how the integration approach must be tailored for the intended application to maximize performance, functionality and minimize cost. We also highlight performance boosters such as heterostructure channel stacks, which offer increased carrier mobility towards improved RF and RF-CMOS performance. Recent progress in tunneling field-effect transistors (TFETs) have enabled the TFET architecture as a process module, allowing for dense integration with MOSFETs on the same chip towards an extremely low-power CMOS technology. Finally, we highlight emerging applications for III-V devices at cryogenic temperatures, where there is a rising need for low-power electronics to support the scaling of quantum computers. The unique properties of III-V, their high mobility and band gap engineering make them highly suitable for this application.

Colour image encryption based on customized neural network and DNA encoding

Cryptography is a method for secure communication by hiding information with secret keys so that only authorised users can read and process it. Efficient random sequence generators provide robust cipher design for cryptographic applications; further, these sequences are used for data encryption. In this paper, the highly chaotic nature of hybrid chaos maps and neural network is combined to build a random number generator for cryptographic applications. A custom neural network with a user-defined layer transfer function is built to increase the generator’s randomness. In this work, the two-hybrid chaotic map’s control parameters and iteration value are designed as a layer transfer function to obtain high randomness. Colour image encryption is performed with the extracted sequences and deoxyribonucleic acid encoding technique. Various tests like NIST, attractor test and correlation are applied to the generator to show the degree of randomness. Simulation analysis such as keyspace, key sensitivity, statistical, differential analysis, and chosen-plaintext attack shows the encryption algorithm’s strength.

Two-phase mass transfer in porous transport layers of the electrolysis cell based on a polymer electrolyte membrane: Analysis of the limitations

Various regimes of gas–liquid flows in porous transport layers/current collectors and their effect on the performance of a model electrolysis cell based on a polymer electrolyte membrane (PEM) are described, based on mathematical simulation. The latter is based on the utilization of a standard description of the two-phase transfer of liquid water, water vapor, and noncondensable gasses in porous media. Numerical calculation with usage of model developed for porous transport layers of an electrolysis cell allowed the determination of the optimal operational parameters for the basic components of the electrolysis cell in dependence of their parameters. Operating conditions causing critical modes of mass transport were identified as well. This study reports of the results of a numerical analysis and discuss consequences for the components of the PEM-based electrolysis cell selection and on its electrical characteristics.

Effect of the flow rate of injected air on the temperature of porous wall in the supersonic flow

The paper is devoted to the experimental analysis of the heat transfer during the injection of air through the porous wall in supersonic airflow. The effect of the injected gas flow rate on the distribution of the porous wall temperature along the mainstream is analyzed at a varying temperature of the injected gas in a wide range from +71.4∘C to −11.2∘C. Mach number of the mainstream equals two, while stagnation temperature equals +20.1∘C. Experimental results show that there is a temperature minimum in the distribution of porous wall temperature, depending on the flow rate of the injected gas. It is established that the particular effect of the injected gas flow rate on the heat transfer significantly depends on the injected gas temperature. The experimental results are analyzed using a simple phenomenological model, taking into account the dissipation of kinetic energy in the turbulent boundary layer, convective heat transfer due to gas injection, and heat transfer between the boundary layer and the porous wall. The competition between convective heat transfer in the turbulent boundary layer and heat transfer between the injected gas and porous wall results in the appearance of a minimum in the dependence of wall temperature on the injected gas flow rate.

(Invited) Layer Transfer Technology for Stacked Multi-Channel Semiconductor-on-Insulator Platform

Continuous scaling of device dimension pushed CMOS technology based on Si materials to its physical limit, alternative high mobility channels such as (Si)Ge and III-V materials have been considered to replace Si to further enhance CMOS performance. In the meantime, ultrathin body (UTB) semiconductor-on-insulator is attractive for fabricating nanowire/nanosheet channel structures for improved gate controllability and immunity against short channel effects in the ultra-scaled high performance and low power dissipation CMOS. To realize multiple channels stacked semiconductor-on-insulator platform on current mainstream Si wafers, layer transfer technology utilizing direct bonding and selective etching was developed to integrate UTB (Si)Ge or InGaAs channels on Si. The devices fabricated with multiple stacked nanowire/nanosheet channels have also been demonstrated through sequential or monolithic 3D integration approach, showing great potential of layer transfer technology for ultimate CMOS structures.

Boundary layer and heat transfer Williamson fluid flow over a stretching sheet with Newtonian heating

The Aligned magnetic field with Williamson fluid has been analyzed using a stretching sheet with Newtonian heating. The governing partial differential equations are transformed to the nonlinear ordinary differential equation by employing the similarity transformations and then solved by using the MATLAB inbuilt solver bvp4c. The influence of various parameters on dimensionless velocity and temperature was graphically explored. Comparisons of all conditions for a particular situation have been made and a very effective agreement has been reached.

Joining of dissimilar metals between magnesium AZ31B and aluminum A6061-T6 using galvanized steel as a transition joining layer

A galvanized steel is used to join Mg AZ31B alloy and Al A6061-T6 alloy as a joining transition layer by cold metal transfer (CMT) method. Firstly, to optimize the process variables, extensive welding tests were performed by a design of experiment method. Then, microstructures, joining mechanisms and tensile properties were characterized and analyzed. Results indicated that Mg and Al alloys can be joined by using galvanized steel as a joining transition layer and cold metal transfer welding method. The formed joint is called as a Mg–steel–Al CMT joint. By using galvanized steel transition joining layer, Mg–Al brittle intermetallics Al12Mg17 and Al3Mg2 were inhibited. The properties of Mg–steel–Al CMT joints have been improved after optimizing the welding variables. The strength of Mg–steel–Al CMT joint is comparable to those of Al–Al joint and Mg–Mg joint. The strength of Mg–steel–Al CMT joint can reach 100% that of Al–Al joint and Al–steel joint, and reach 90% that of Mg–Mg joint and Mg–steel joint. The optimized Mg–steel–Al CMT welded joint with galvanized steel transition layer is fractured at the heat affected zone of Al base metal rather than at the weld-brazed interface, due to softening of Al base metal.

Learning Functionally Decomposed Hierarchies for Continuous Control Tasks With Path Planning

We present HiDe, a novel hierarchical reinforcement learning architecture that successfully solves long horizon control tasks and generalizes to unseen test scenarios. Functional decomposition between planning and low-level control is achieved by explicitly separating the state-action spaces across the hierarchy, which allows the integration of task-relevant knowledge per layer. We propose an RL-based planner to efficiently leverage the information in the planning layer of the hierarchy, while the control layer learns a goal-conditioned control policy. The hierarchy is trained jointly but allows for the modular transfer of policy layers across hierarchies of different agents. We experimentally show that our method generalizes across unseen test environments and can scale to 3x horizon length compared to both learning and non-learning based methods. We evaluate on complex continuous control tasks with sparse rewards, including navigation and robot manipulation.

Layer-Scale and Chip-Scale Transfer Techniques for Functional Devices and Systems: A Review.

Hetero-integration of functional semiconductor layers and devices has received strong research interest from both academia and industry. While conventional techniques such as pick-and-place and wafer bonding can partially address this challenge, a variety of new layer transfer and chip-scale transfer technologies have been developed. In this review, we summarize such transfer techniques for heterogeneous integration of ultrathin semiconductor layers or chips to a receiving substrate for many applications, such as microdisplays and flexible electronics. We showed that a wide range of materials, devices, and systems with expanded functionalities and improved performance can be demonstrated by using these technologies. Finally, we give a detailed analysis of the advantages and disadvantages of these techniques, and discuss the future research directions of layer transfer and chip transfer techniques.

Mathematical and artificial neural network modeling for vacuum drying kinetics of Moringa olifera leaves followed by determination of energy consumption and mass transfer parameters

Mapping the drying characteristics of biological products is essential for drying time estimation and reduction of energy consumption. The knowledge of mass transfer parameters during different drying conditions is required for process and equipment design and is of great industrial importance. In this work, an Artificial Neural Network (ANN) approach was adopted to model the vacuum drying kinetics of moringa leaves. Levenberg–Marquardt’s training algorithm with LOGSIGMOID and TANSIGMOID hidden layer transfer functions gave superior results for the prediction of moisture content and moisture ratio, respectively. Further, a comparative evaluation of the predictive capability of ANN and 7 different semi-empirical models was performed. The Page model was found suitable to fit the experimental data with a R2 comparable to that of ANN. However, the MSE observed for ANN (1.05 × 10−6) was significantly lower than that of Page model (2.56 × 10-6 to 5.81 × 10-4). Effective moisture diffusivity and mass transfer coefficient increased with increase in temperature from 0.71 × 10-9 to 1.91 × 10-9 m2/s and, 1.07 × 10-7 to 4.07 × 10-7 m/s, respectively. Activation energy for drying of moringa leaves was calculated as 42.84 kJ/mol which showed moderate energy requirements for moisture diffusion. Specific energy consumed was directly affected by drying time and varied from 6.07 to 22.26 kW h/kg. Drying temperature of 60 °C resulted in higher drying rate, lower drying time and energy consumption and therefore, recommended for drying of Moringa olifera leaves.

Modeling and optimization of small-scale NF/RO seawater desalination using the artificial neural network (ANN)

The performance of seawater hybrid NF/RO desalination plant including permeate conductivity; permeate flow rate and permeate recovery. Under different feed parameters time, inlet temperature, inlet pressure, inlet conductivity and inlet flow rate were modelled by Artificial Neural Network (ANN) back-propagation based on Levenberg– Marquardt training algorithm. The optimal ANN model had a 5-8-3 architecture with a hyperbolic tangent transfer function in hidden layer and linear transfer function at the output layer. The ability of ANN performed model was compared with multiple linear regression (MLR). The results show that MLR is not satisfactory for predicting the performance of NF/RO hybrid desalination process with a correlation coefficient about 0.6. The trained ANN model has presented a good agreement between the prediction and the experimental data during the training with reasonable statistical metrics values (RMSE, MAE and AARD). The coefficient of determination values for the prediction of permeate conductivity, permeate flow rate and recovery by ANN were 0.969, 0.942, and 0.963, respectively. Therefore, the ANN model can successfully predict the performance of NF/RO hybrid seawater desalination plant.

Robust semiconductor-on-ferroelectric structures with hafnia–zirconia–alumina UTBOX stacks compatible with CMOS technology

Silicon wafers with an ultrathin buried high-k oxide were fabricated by the atomic layer deposition of high-k layers on sapphire and silicon substrates with subsequent silicon layer transfer onto their surfaces by bonding and rapid thermal annealing (RTA). An extremely high thermal stability of hafnia orthorhombic Pca21 ferroelectric phase of up to 1100 °C was observed in the silicon-on-ferroelectric structure on the sapphire substrate. Silicon–ferroelectric–silicon structures with hafnia BOX and alumina inclusions also demonstrated increased thermal stability for hafnia or hafnia–zirconia alloys during the RTA treatment up to 900 °C, which makes them fully compatible with current complementary metal oxide semiconductor technology, promising integrated circuits for neuromorphic computation and optoelectronic switching devices.

All-parylene flexible wafer-scale graphene thin film transistor

Graphene is an ideal candidate as a component of flexible/wearable electronics due to its two-dimensional nature and low gate bias requirements for high quality devices. However, the proven methods for fabrication of graphene thin film transistors (TFTs) on fixed substrates involve using a sacrificial polymer layer to transfer graphene to a desired surface have led to mixed results for flexible devices. Here, by using the same polymer layer (parylene C) for both graphene transfer and the flexible substrate itself, we produced graphene TFTs on the wafer-scale requiring less than |2 V| gate bias and with high mechanical resilience of 30,000 bending cycles.

Mathematical modeling and parameter estimation of MFI membranes for para/ortho‐xylene separation

AbstractA mathematical model of a membrane with a thin, oriented, and selective MFI layer, which also includes contributions from defects, pore blockages, support layer, and external mass transfer, was formulated based on the Maxwell‐Stefan equations. It was validated using reported (Kim et al., Angew Chemie Int Ed, 2018, 57:480–485; Jeon et al., Nature, 2017, 543:690–694) para/ortho‐xylene separation data from five MFI membranes. The diffusivities of the xylenes were considered to be the same for all membranes, while the thickness and the density of defects and pore blockage were treated as unique properties of each membrane, and their contributions were estimated by fitting the model to the corresponding separation data. The effects of these properties and the role of permeate pressure on the separation performance were subsequently analyzed. The proposed modeling, parameter estimation, and analysis framework allow one to quantitatively interpret the variation of separation performance, to understand separation bottlenecks, and to provide guidance for designing membranes with desired performance.