A Ti:Al2O3 master-oscillator/power-amplifiersystem

The authors have constructed a multistage Ti:Al₂O₃ master-oscillator/power-amplifier system which generates 115-ns, 0.38-J pulses at 800 nm. The system is tunable from 760 to 825 nm and has a repetition rate of 10 Hz. Measurements of the output pulse demonstrate near-diffraction-limited performance and a Fourier-transform-limited bandwidth of ~4 MHz     

3D Knitting for Pneumatic Soft Robotics

Soft robots adapt passively to complex environments due to their inherent compliance, allowing them to interact safely with fragile or irregular objects and traverse uneven terrain. The vast tunability and ubiquity of textiles has enabled new soft robotic capabilities, especially in the field of wearable robots, but existing textile processing techniques (e.g., cut-and-sew, thermal bonding) are limited in terms of rapid, additive, accessible, and waste-free manufacturing. While 3D knitting has the potential to address these limitations, an incomplete understanding of the impact of structure and material on knit-scale mechanical properties and macro-scale device performance has precluded the widespread adoption of knitted robots. In this work, the roles of knit structure and yarn material properties on textile mechanics spanning three regimes–unfolding, geometric rearrangement, and yarn stretching–are elucidated and shown to be tailorable across unique knit architectures and yarn materials. Based on this understanding, 3D knit soft actuators for extension, contraction, and bending are constructed. Combining these actuation primitives enables the monolithic fabrication of entire soft grippers and robots in a single-step additive manufacturing procedure suitable for a variety of applications. This approach represents a first step in seamlessly “printing” conformal, low-cost, customizable textile-based soft robots on-demand.     

3D monolithic integration: Technological challenges and electrical results

After a short reminder of the principle of monolithic 3D integration, this paper firstly reviews the main technological challenges associated to this integration and proposes solutions to assess them. Wafer bonding is used to have perfect crystalline quality of the top layer at the wafer scale. Thermally stabilized silicide is developed to use standard salicidation scheme in the bottom layer. Finally a fully depleted SOI low temperature process is demonstrated for top layer processing (overall temperature kept below 650 °C). In a second part the electrical results obtained within this integration scheme are summarized: mixed Ge over Si invertor is demonstrated and electrostatic coupling between top and bottom layer is used to shift the threshold voltage of the top layer. Finally circuit opportunities such as stabilized SRAM or gain in density are investigated.     

Measurement of mobility in HEMT devices using high-order derivatives

In this paper, a novel approach to the measurement of mobility of GaAs HEMT devices is presented. The new approach employs high-order derivatives as a means of determining the parameters of the proposed new mobility equation. The new approach is compared to established mobility measurement methods, and shown to offer better accuracy. The results presented also consider the behavior of mobility in the linear and saturation bias regions. The mobility value extracted by this new method has permitted improvements to the MESFET/HEMT model when simulating the behavior of the device in the linear region. This is critical in many applications, such as in low current linear-mixing applications.     

Textile Technology for Soft Robotic and Autonomous Garments

Textiles have emerged as a promising class of materials for developing wearable robots that move and feel like everyday clothing. Textiles represent a favorable material platform for wearable robots due to their flexibility, low weight, breathability, and soft hand-feel. Textiles also offer a unique level of programmability because of their inherent hierarchical nature, enabling researchers to modify and tune properties at several interdependent material scales. With these advantages and capabilities in mind, roboticists have begun to use textiles, not simply as substrates, but as functional components that program actuation and sensing. In parallel, materials scientists are developing new materials that respond to thermal, electrical, and hygroscopic stimuli by leveraging textile structures for function. Although textiles are one of humankind's oldest technologies, materials scientists and roboticists are just beginning to tap into their potential. This review provides a textile-centric survey of the current state of the art in wearable robotic garments and highlights metrics that will guide materials development. Recent advances in textile materials for robotic components (i.e., as sensors, actuators, and integration components) are described with a focus on how these materials and technologies set the stage for wearable robots programmed at the material level.     

Band Selection in Multispectral Images by Minimization of Dependent Information

In this paper, a band selection technique for hyperspectral image data is proposed. Supervised feature extraction techniques allow a reduction of the dimensionality to extract relevant features through a labeled training set. This implies an analysis of the existing class distributions, which usually means, in the case of hyperspectral imaging, a large number of samples, making the labeling process difficult. A possible alternative could be the use of information measures, which are the basis of the proposed method. The present approach basically behaves as an unsupervised feature selection criterion, to obtain the relevant spectral bands from a set of sample images. The relations of information content between spectral bands are analyzed, leading to the proposed technique based on the minimization of the dependent information between spectral bands, while trying to maximize the conditional entropies of the selected bands     

Lead‐Free Polycrystalline Ferroelectric Nanowires with Enhanced Curie Temperature

Ferroelectrics are important technological materials with wide‐ranging applications in electronics, communication, health, and energy. While lead‐based ferroelectrics have remained the predominant mainstay of industry for decades, environmentally friendly lead‐free alternatives are limited due to relatively low Curie temperatures (T _C) and/or high cost in many cases. Efforts have been made to enhance T _C through strain engineering, often involving energy‐intensive and expensive fabrication of thin epitaxial films on lattice‐mismatched substrates. Here, a relatively simple and scalable sol–gel synthesis route to fabricate polycrystalline (Ba_0.85Ca_0.15)(Zr_0.1Ti_0.9)O₃ nanowires within porous templates is presented, with an observed enhancement of T _C up to ≈300 °C as compared to ≈90 °C in the bulk. By combining experiments and theoretical calculations, this effect is attributed to the volume reduction in the template‐grown nanowires that modifies the balance between different structural instabilities. The results offer a cost‐effective solution‐based approach for strain‐tuning in a promising lead‐free ferroelectric system, thus widening their current applicability.     

Characterization of MIS structures and PTFTs using TiOx deposited by spin-coating

This paper presents a method to deposit titanium oxide (TiOx) films from a sol containing IV titanium isopropoxide Ti[OCH(CH₃)₂]₄, 2-methoxyethanol, CH₃OCH₂CH₂OH and ethanolamine H₂NCH₂CH₂OH, in order to obtain layers with thickness above 220 nm with the required characteristics to be used in Metal–Insulator–Semiconductor, MIS, structures and polymeric thin film transistors, PTFTs. The effect of using different component ratios is described. The dielectric constant was in the order of 12, the critical electric field was 5 × 10⁵ V/cm and the density of states at the interface was less than 1 × 10¹¹ cm⁻². The analysis of MIS structures prepared with these TiOx layers shows that they are suitable for using in PTFTs. The fabrication of independent bottom gate PTFTs with poly(3-hexylthiophene), P3HT, on top of the TiOx layer is described, obtaining a major reduction in the operation voltage range from −30 V to −4 V, while maintaining the typical mobility for P3HT PTFTs.     

Ascertaining the importance of neurons to develop better brain-machine interfaces

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.