The biological nervous system possesses a powerful information processing capability, and only needs a partial signal stimulation to perceive the entire signal. Likewise, the hardware implementation of an information processing system with similar capabilities is of great significance, for reducing the dimensions of data from sensors and improving the processing efficiency. Here, it is reported that indium‐gallium‐zinc‐oxide thin film phototransistors exhibit the optoelectronic switching and light‐tunable synaptic characteristics for in‐sensor compression and computing. Phototransistor arrays can compress the signal while sensing, to realize in‐sensor compression. Additionally, a reservoir computing network can also be implemented via phototransistors for in‐sensor computing. By integrating these two systems, a neuromorphic system for high‐efficiency in‐sensor compression and computing is demonstrated. The results reveal that even for cases where the signal is compressed by 50%, the recognition accuracy of reconstructed signal still reaches ≈96%. The work paves the way for efficient information processing of human–computer interactions and the Internet of Things.
Optoelectronic synaptic devices capable of processing multiwavelength inputs are critical for neuromorphic vision hardware, which remains an important challenge. Here, we develop a bidirectional synaptic phototransistor based on a two-dimensional ferroelectric semiconductor of α-In 2 Se 3 , which exhibits bidirectional potentiated and depressed synaptic weight update under optical pulse stimulation. Importantly, the bidirectional optoelectronic synaptic behavior can be extended to multiwavelengths (blue, green, and red light), which could be used for color recognition. The mechanism underlying the bidirectional synaptic characteristics is attributed to the gateconfigurable barrier heights as revealed by the Kelvin probe force microscopy measurement. The α-In 2 Se 3 device exhibits versatile synaptic plasticity such as paired-pulse facilitation, short-and long-term potentiation, and long-term depression. The bidirectional optoelectronic synaptic weight updates under multiwavelength inputs enable a high accuracy of 97% for mixed color pattern recognition.
With the rapid progress of artificial intelligence, various perception networks were constructed to enable Internet of Things (IoT) applications, thereby imposing formidable challenges to communication bandwidth and information security. Memristors, which exhibit powerful analog computing capabilities, emerged as a promising solution expected to address these challenges by enabling the development of the next-generation high-speed digital compressed sensing (CS) technologies for edge computing. However, the mechanisms and fundamental properties of memristors for achieving CS remain unclear, and the underlying principles for selecting different implementation methods based on various application scenarios have yet to be elucidated. A comprehensive overview of memristor-based CS techniques is currently lacking. In this article, we systematically presented CS requirements on device performance and hardware implementation. The relevant models were analyzed and discussed from the mechanism level to elaborate the memristor CS system scientifically. In addition, the method of deploying CS hardware using the powerful signal processing capabilities and unique performance of memristors was further reviewed. Subsequently, the potential of memristors in all-in-one compression and encryption was anticipated. Finally, existing challenges and future outlooks for memristor-based CS systems were discussed.
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