In‐Sensor Computing: Materials, Devices, and Integration Technologies

Abstract

The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in‐sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in‐sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in‐sensor computing is provided.

In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array

“…1a) [12][13][14] . In addition, these systems utilize additional optical filters for charge-coupled devices (CCDs) and complementary metal-oxidesemiconductor (CMOS) image sensors, increasing the complexity of the entire system for latent fingerprint identification [15][16][17] . Therefore, to simplify the system construction and enhance the processing efficiency of the DUV fingerprint recognition system, it is urgent to develop a new principle device and new computing architecture.…”

Progress of Materials and Devices for Neuromorphic Vision Sensors

“…During photosensing, the charge-trapping sites in the photosensor mainly play negative roles, such as slowing down detection and reducing sensitivity. However, some researchers have intentionally introduced charge trap sites within the device, which enables the development of environmentally adaptive photosensors based on sub-linear photoresponse properties [ 104 ]. Here, sub-linear photoresponse means that the magnitude of the photocurrent generated from the low (high) light input is enhanced (reduced), compared to the case where the photocurrent and the incident light intensity have a linear relationship.…”

Temporal Modulation and Synergistic Current Response of Zinc-Tin Oxide/Gold Nanoparticle Phototransistors Triggered by an Electrical–Optical Hybrid Pulse

“…5,6 The processing-in-sensor techniques provide an unparalleled opportunity to process complex and temporal data in chaotic systems. 7,8 However, to realize temporal processing hardware, the states of devices shall be determined by the features in the temporal inputs, including the present input and a period of input in the past.…”

“…As regards the hardware architecture, the development of in-sensor processing is able to optimize data transfer and computation between the processors and sensors. , The visual pre-processing in the sensor network can improve the power consumption on data conversion and transmission and also boost the image recognition rate . In addition, the time-series forecasting can be tackled by machine learning algorithms based on reservoir computing owing to its capability to map diverse features at different timescales, where a reservoir model includes a sensory-based time-varying input stream. , The processing-in-sensor techniques provide an unparalleled opportunity to process complex and temporal data in chaotic systems. , However, to realize temporal processing hardware, the states of devices shall be determined by the features in the temporal inputs, including the present input and a period of input in the past.…”