Bio‐Inspired In‐Sensor Compression and Computing Based on Phototransistors

Abstract

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.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

“…In recent years, there has been an endless stream of research on simulating bio-neural systems with iontronic/electronic devices for neuromorphic sensing and computing. [89][90][91][92][93][94][95] From the CNS to the PNS, the signal processing and memory are facilitated by regulating flux and polarization of ion species (Na + , Cl − , K + , Ca 2+ , etc.) inside synapses, which can regulate the signal strength that one neuron can pass on to the next (Figure 2a).…”

Biocompatible Material‐Based Flexible Biosensors: From Materials Design to Wearable/Implantable Devices and Integrated Sensing Systems

“…[18] In addition, for in-sensor computing at the array level, multiple sensors can interact with external stimuli simultaneously, making the sensing array element a computational unit and contributing to high parallelism. [209] And the physical coupling between different sensors can provide high computational complexity. [18] In-sensor computing has significant implications for the development of flexible sensor devices for biological systems.…”

“…Frequent transmission of these data between sensors and computing units can lead to limitations in energy efficiency, speed, and security. [ 209 ] Therefore, designing and developing new sensing computing architectures that integrate computing functions into sensor networks can improve the efficiency of data processing. In‐sensor computing transfers the processing of data to the sensing terminal, allowing sensor devices to in situ respond to external stimuli and output different characteristics (Figure 21aii).…”

All‐in‐One Compression and Encryption Engine Based on Flexible Polyimide Memristor

“…[2] CS uses the structure of specific signals to perform data compression and acquisition simultaneously at the physical interface of the analog and digital domains via a random encoding process, allowing acquisition at sub-Nyquist rates. [3][4][5] Specifically, a Φ-matrix, also known as a measurement matrix, is used to complete the random encoding process. Any receiver attempting to decode CS measurements must be aware of the actual encoding matrix used during the acquisition to recover the signal accurately.…”