Atsushi Omote

Tailoring effective thermoelectric tensors and high-density power generation in a tubular Bi0.5Sb1.5Te3/Ni composite with cylindrical anisotropy

Transverse thermoelectric responses in heterogeneous composites made of periodically laminated Bi0.5Sb1.5Te3/Ni in a tubular shape were investigated. Numerical calculations quantitatively clarify the relationship between geometrical parameters and effective thermoelectric tensors. In the present tubular heterogeneous composites, the temperature gradient across the radial direction yields a transverse voltage along the axial direction due to the unnatural cylindrical anisotropy. The tubular configuration allows for direct and efficient heat transfer from fluid heat sources. A high-density power generation of 417 W m−2 was achieved under the small temperature difference of 83 K.

Power-efficient low-temperature woven coiled fibre actuator for wearable applications

A fibre actuator that generates a large strain with high specific power represents a promising strategy to develop novel wearable devices and robotics. We propose a new coiled-fibre actuator based on highly drawn, hard linear low-density polyethylene (LLDPE) fibres. Driven by resistance heating, the actuator can be operated at temperatures as low as 60 °C and uses only 20% of the power consumed by previously coiled fibre actuators when generating 20 MPa of stress at 10% strain. In this temperature range, 1600 W kg−1 of specific work (8 times that of a skeletal muscle) at 69 MPa of tensile stress (230 times that of a skeletal muscle) with a work efficiency of 2% is achieved. The actuator generates strain as high as 23% at 90 °C. Given the low driving temperature, the actuator can be combined with common fabrics or stretchable conductive elastomers without thermal degradation, allowing for easy use in wearable systems. Nanostructural analysis implies that the lamellar crystals in drawn LLDPE fibres are weakly bridged with each other, which allows for easy deformation into compact helical shapes via twisting and the generation of large strain with high work efficiency.

Back-Propagation Operation for Analog Neural Network Hardware with Synapse Components Having Hysteresis Characteristics

To realize an analog artificial neural network hardware, the circuit element for synapse function is important because the number of synapse elements is much larger than that of neuron elements. One of the candidates for this synapse element is a ferroelectric memristor. This device functions as a voltage controllable variable resistor, which can be applied to a synapse weight. However, its conductance shows hysteresis characteristics and dispersion to the input voltage. Therefore, the conductance values vary according to the history of the height and the width of the applied pulse voltage. Due to the difficulty of controlling the accurate conductance, it is not easy to apply the back-propagation learning algorithm to the neural network hardware having memristor synapses. To solve this problem, we proposed and simulated a learning operation procedure as follows. Employing a weight perturbation technique, we derived the error change. When the error reduced, the next pulse voltage was updated according to the back-propagation learning algorithm. If the error increased the amplitude of the next voltage pulse was set in such way as to cause similar memristor conductance but in the opposite voltage scanning direction. By this operation, we could eliminate the hysteresis and confirmed that the simulation of the learning operation converged. We also adopted conductance dispersion numerically in the simulation. We examined the probability that the error decreased to a designated value within a predetermined loop number. The ferroelectric has the characteristics that the magnitude of polarization does not become smaller when voltages having the same polarity are applied. These characteristics greatly improved the probability even if the learning rate was small, if the magnitude of the dispersion is adequate. Because the dispersion of analog circuit elements is inevitable, this learning operation procedure is useful for analog neural network hardware.

Conductivity Modulation of Gold Thin Film at Room Temperature via All-Solid-State Electric-Double-Layer Gating Accelerated by Nonlinear Ionic Transport

We demonstrated the field-effect conductivity modulation of a gold thin film by all-solid-state electric-double-layer (EDL) gating at room temperature using an epitaxially grown oxide fast lithium conductor, La2/3-xLi3xTiO3 (LLT), as a solid electrolyte. The linearly increasing gold conductivity with increasing gate bias demonstrates that the conductivity modulation is indeed due to carrier injection by EDL gating. The response time becomes exponentially faster with increasing gate bias, a result of the onset of nonlinear ionic transportation. This nonlinear dynamic response indicates that the ionic motion-driven device can be much faster than would be estimated from a linear ionic transport model.

Battery-less impact-logging device consisting of a vibration energy scavenger and ferroelectric memory

Energy scavenging from ambient energy sources has been widely researched with a focus on sensors and devices as alternative power sources for batteries. Vibration energy scavenging is of specific interest for a variety of environments in which sinusoidal vibrations or repetitive impacts are present [1]. In addition, a vibration energy scavenger has applicability to an impact sensor because an applied impact is immediately converted to electrical power. If the impact data are stored at the same time in non-volatile memory by the electrical power generated by impact itself, a battery-less impact-logging system is feasible. However, the generated power alone is insufficient for recording impact histories to conventional non-volatile memory. In this work, we focus on a ferroelectric-gate field-effect transistor (FeFET) as a non-volatile memory [2] because an FeFET is able to memorize data with low power consumption and read the data non-destructively.

Off-diagonal thermoelectric effect in tilted layered materials

Off-diagonal thermoelectric effect is an unconventional thermoelectric phenomenon which emerges in layered materials with tilted alignment. Here, we report development of unique functionalities using the off-diagonal thermoelectric effect in two different material systems; (i) tilted-oriented thin films of layered cobaltite Ca<inf>x</inf>CoO<inf>2</inf>, and (ii) artificial tilted multilayer of Bi/Cu and Bi<inf>0.5</inf>Sb<inf>1.5</inf>Te<inf>3</inf>/Ni. Ca<inf>x</inf>CoO<inf>2</inf> thin films enabled generation of an extremely large film in-plane voltage of 1 V by introducing a unit temperature difference in the film out-of-plane. On the other hand, Bi/Cu multilayer showed enhanced power factor of 50.1 µW/cmK², which was ∼1.5 times larger than that of the constituent Bi alone. We also realized high electrical power generation of 1.3 W in the Bi<inf>0.5</inf>Sb<inf>1.5</inf>Te<inf>3</inf>/Ni multilayer fabricated in a tubular structure. These features provide potential applications to sensitive optical/thermal sensors and high-power thermoelectric generators.