Monolithic Integration

Venghaus, H.; Bach, H.-G.; Fidorra, F.; Heidrich, H.; Kaiser, R.; Weinert, C.M.

doi:10.1007/978-3-642-56466-6_11

Cited by 1 publication

(1 citation statement)

References 48 publications

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“…2 The indiscriminate transmission of sensor data to the terminal causes excessive pressure on the processor, 3,4 highlighting the need for novel devices that can acquire, analyze, and respond to external information more efficiently, especially in object detection and tracking scenarios. 5 In the human visual system, nerve cells in the retina encode external visual information into nerve signals and undergo primary classification processes like noise reduction filtering and contour enhancement. 6 The parallel processing capabilities of the human brain through synapses and neurons promote ultralow energy consumption during learning and memory processes.…”

Section: ■ Introductionmentioning

confidence: 99%

Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception

Liu,

Wu,

et al. 2024

ACS Appl. Mater. Interfaces

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The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.

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