Ultrasensitive Near-Infrared InAs Colloidal Quantum Dot-ZnON Hybrid Phototransistor Based on a Gradated Band Structure.

Amorphous metal oxide semiconductor phototransistors (MOTPs) integrated with colloidal quantum dots (QDs) (QD-MOTPs) are promising infrared photodetectors owing to their high photoconductive gain, low off-current level, and high compatibility with pixel circuits. However, to date, the poor mobility of conventional MOTPs, such as indium gallium zinc oxide (IGZO), and the toxicity of lead (Pb)-based QDs, such as lead sulfide and lead selenide, has limited the commercial applications of QD-MOTPs. Herein, an ultrasensitive QD-MOTP fabricated by integrating a high-mobility zinc oxynitride (ZnON)-based MOTP and lead-free indium arsenide (InAs) QDs is demonstrated. A new gradated bandgap structure is introduced in the InAs QD layer that absorbs infrared light, which prevents carriers from moving backward and effectively reduces electron-hole recombination. Chemical, optical, and structural analyses confirm the movement of the photoexcited carriers in the graded band structure. The novel QD-MOTP exhibits an outstanding performance with a responsivity of 1.15 × 105 A W-1 and detectivity of 5.32 × 1016 Jones at a light power density of 2 µW cm-2 under illumination at 905 nm.

A Multimodal Neural-Recording IC With Reconfigurable Analog Front-Ends for Improved Availability and Usability for Recording Channels.

In this work, we present an 8-channel reconfigurable multimodal neural-recording IC, which provides improved availability and usability of recording channels in various experiment scenarios. Each recording channel changes its configuration depending on whether the channel is assigned to record voltage or current signal. As a result, although the total number of channels is fixed by design, the channels utilized for voltage and current recording can be set freely and optimally for given experiment targets, scenarios, and circumstances, maximizing the availability and usability of recording channels.The proposed concept was demonstrated by fabricating the IC using a standard 180-nm CMOS process.Using the IC, we successfully performed an in vivo experiment from the hippocampal area of a mouse brain. The measured input noise of the reconfigurable front-end is 4.75 µVrms at voltage-recording mode and 7.4 pArms at current-recording mode while consuming 5.72 µW/channel.

Development of prediction models of spontaneous ureteral stone passage through machine learning: Comparison with conventional statistical analysis.

OBJECTIVES: To develop a prediction model of spontaneous ureteral stone passage (SSP) using machine learning and logistic regression and compare the performance of the two models. Indications for management of ureteral stones are unclear, and the clinician determines whether to wait for SSP or perform active treatment, especially in well-controlled patients, to avoid unwanted complications. Therefore, suggesting the possibility of SSP would help make a clinical decision regarding ureteral stones. METHODS: Patients diagnosed with unilateral ureteral stones at our emergency department between August 2014 and September 2018 were included and underwent non-contrast-enhanced computed tomography 4 weeks from the first stone episode. Predictors of SSP were applied to build and validate the prediction model using multilayer perceptron (MLP) with the Keras framework. RESULTS: Of 833 patients, SSP was observed in 606 (72.7%). SSP rates were 68.2% and 75.6% for stone sizes 5-10 mm and <5 mm, respectively. Stone opacity, location, and whether it was the first ureteral stone episode were significant predictors of SSP. Areas under the curve (AUCs) for receiver operating characteristic (ROC) curves for MLP, and logistic regression were 0.859 and 0.847, respectively, for stones <5 mm, and 0.881 and 0.817, respectively, for 5-10 mm stones. CONCLUSION: SSP prediction models were developed in patients with well-controlled unilateral ureteral stones; the performance of the models was good, especially in identifying SSP for 5-10-mm ureteral stones without definite treatment guidelines. To further improve the performance of these models, future studies should focus on using machine learning techniques in image analysis.

Realization of an Artificial Visual Nervous System using an Integrated Optoelectronic Device Array.

Human behavior (e.g., the response to any incoming information) has very complex forms and is based on the response to consecutive external stimuli entering varied sensory receptors. Sensory adaptation is an elementary form of the sensory nervous system known to filter out irrelevant information for efficient information transfer from consecutive stimuli. As bioinspired neuromorphic electronic system is developed, the functionality of organs shall be emulated at a higher level than the cell. Because it is important for electronic devices to possess sensory adaptation in spiking neural networks, the authors demonstrate a dynamic, real-time, photoadaptation process to optical irradiation when repeated light stimuli are presented to the artificial photoreceptor. The filtered electrical signal generated by the light and the adapting signal produces a specific range of postsynaptic states through the neurotransistor, demonstrating changes in the response according to the environment, as normally perceived by the human brain. This successfully demonstrates plausible biological sensory adaptation. Further, the ability of this circuit design to accommodate changes in the intensity of bright or dark light by adjusting the sensitivity of the artificial photoreceptor is demonstrated. Thus, the proposed artificial photoreceptor circuits have the potential to advance neuromorphic device technology by providing sensory adaptation capabilities.

Subthreshold electrical stimulation as a low power electrical treatment for stroke rehabilitation.

As a promising future treatment for stroke rehabilitation, researchers have developed direct brain stimulation to manipulate the neural excitability. However, there has been less interest in energy consumption and unexpected side effect caused by electrical stimulation to bring functional recovery for stroke rehabilitation. In this study, we propose an engineering approach with subthreshold electrical stimulation (STES) to bring functional recovery. Here, we show a low level of electrical stimulation boosted causal excitation in connected neurons and strengthened the synaptic weight in a simulation study. We found that STES with motor training enhanced functional recovery after stroke in vivo. STES was shown to induce neural reconstruction, indicated by higher neurite expression in the stimulated regions and correlated changes in behavioral performance and neural spike firing pattern during the rehabilitation process. This will reduce the energy consumption of implantable devices and the side effects caused by stimulating unwanted brain regions.

Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing.

The development of brain-inspired neuromorphic computing, including artificial intelligence (AI) and machine learning, is of considerable importance because of the rapid growth in hardware and software capacities, which allows for the efficient handling of big data. Devices for neuromorphic computing must satisfy basic requirements such as multilevel states, high operating speeds, low energy consumption, and sufficient endurance, retention and linearity. In this study, inorganic perovskite-type amorphous strontium vanadate (a-SrVOx: a-SVO) synthesized at room temperature is utilized to produce a high-performance memristor that demonstrates nonvolatile multilevel resistive switching and synaptic characteristics. Analysis of the electrical characteristics indicates that the a-SVO memristor illustrates typical bipolar resistive switching behavior. Multilevel resistance states are also observed in the off-to-on and on-to-off transition processes. The retention resistance of the a-SVO memristor is shown to not significantly change for a period of 2 × 104 s. The conduction mechanism operating within the Ag/a-SVO/Pt memristor is ascribed to the formation of Ag-based filaments. Nonlinear neural network simulations are also conducted to evaluate the synaptic behavior. These results demonstrate that a-SVO-based memristors hold great promise for use in high-performance neuromorphic computing devices.

Enhancement of Interface Characteristics of Neural Probe Based on Graphene, ZnO Nanowires, and Conducting Polymer PEDOT.

In the growing field of brain-machine interface (BMI), the interface between electrodes and neural tissues plays an important role in the recording and stimulation of neural signals. To minimize tissue damage while retaining high sensitivity, a flexible and a smaller electrode with low impedance is required. However, it is a major challenge to reduce electrode size while retaining the conductive characteristics of the electrode. In addition, the mechanical mismatch between stiff electrodes and soft tissues creates damaging reactive tissue responses. Here, we demonstrate a neural probe structure based on graphene, ZnO nanowires, and conducting polymer that provides flexibility and low impedance performance. A hybrid Au and graphene structure was utilized to achieve both flexibility and good conductivity. Using ZnO nanowires to increase the effective surface area drastically decreased the impedance value and enhanced the signal-to-noise ratio (SNR). A poly[3,4-ethylenedioxythiophene] (PEDOT) coating on the neural probe improved the electrical characteristics of the electrode while providing better biocompatibility. In vivo neural signal recordings showed that our neural probe can detect clearer signals.