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.

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.

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.