Transformative Multifunction Deep Ultraviolet Photodetectors for On-Demand Applications: From Fast Optical Communication to Tunable In-Sensor Photocurrent Integration

The strategic utilization of photodetectors' transient response could open new frontiers from free-space optical communication to the emerging field of neuromorphic optoelectronics. Contrarily, while communication requires a fast response, neuromorphic applications benefit from a slow and integrative transient photocurrent. By integrating these functionalities in a single device, this study unveils a photodetector with tunable responses, bridging the gap between optical communication and neuromorphic sensing and creating a versatile platform with on-demand applications. Particularly, a Ga2O3-based photodetector was designed, exhibiting a photocurrent on/off ratio close to 104, high responsivity of 0.43 A/W, and detectivity 1.22 × 1013 Jones under deep ultraviolet illumination (λ ∼ 260 nm). The photodetector demonstrates transient time-dependent on operational voltage, ranging from 10-4 to 0.2 s. The underlying mechanism is attributed to the voltage-dependent balance between photocarrier generation and defect-related recombination, as revealed by electrostatic force microscopy. Additionally, we have demonstrated potential applications, including digital Morse code interpretation, tunable integration of optical inputs within the sensor, one-time readouts, and effective analog Morse code reading. Furthermore, the effectiveness of input information recognition using analog integration, even with anomalies, was demonstrated. This work establishes a versatile approach for tunable in-sensor optical processing, potentially useful for a wide range of applications, from free-space optical communication to neuromorphic sensing.

Nanomaterial-Based Artificial Vision Systems: From Bioinspired Electronic Eyes to In-Sensor Processing Devices

High-performance robotic vision empowers mobile and humanoid robots to detect and identify their surrounding objects efficiently, which enables them to cooperate with humans and assist human activities. For error-free execution of these robots' tasks, efficient imaging and data processing capabilities are essential, even under diverse and complex environments. However, conventional technologies fall short of meeting the high-standard requirements of robotic vision under such circumstances. Here, we discuss recent progress in artificial vision systems with high-performance imaging and data processing capabilities enabled by distinctive electrical, optical, and mechanical characteristics of nanomaterials surpassing the limitations of traditional silicon technologies. In particular, we focus on nanomaterial-based electronic eyes and in-sensor processing devices inspired by biological eyes and animal visual recognition systems, respectively. We provide perspectives on key nanomaterials, device components, and their functionalities, as well as explain the remaining challenges and future prospects of the artificial vision systems.

From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications

The Internet of Things has grown quickly in the last few years, with a variety of sensing, processing and storage devices interconnected, resulting in high data traffic. While some sensors such as temperature, or humidity sensors produce a few bits of data periodically, imaging sensors output data in the range of megabytes every second. This raises a complexity for battery operated smart cameras, as they would be required to perform intensive image processing operations on large volumes of data, within energy consumption constraints. By using intelligence partitioning we analyse the effects of different partitioning scenarios for the processing tasks between the smart camera node, the fog computing layer and cloud computing, in the node energy consumption as well as the real time performance of the WVSN (Wireless Vision Sensor Node). The results obtained show that traditional design space exploration approaches are inefficient for WVSN, while intelligence partitioning enhances the energy consumption performance of the smart camera node and meets the timing constraints.