All‐Inorganic Perovskite Quantum‐Dot Optical Neuromorphic Synapses for Near‐Sensor Colored Image Recognition

As the demand for the neuromorphic vision system in image recognition experiences rapid growth, it is imperative to develop advanced architectures capable of processing perceived data proximal to sensory terminals. This approach aims to reduce data movement between sensory and computing units, minimizing the need for data transfer and conversion at the sensor-processor interface. Here, an optical neuromorphic synaptic (ONS) device is demonstrated by homogeneously integrating optical-sensing and synaptic functionalities into a unified material platform, constructed exclusively by all-inorganic perovskite CsPbBr<sub>3</sub> quantum dots (QDs). The dual functionality of each unit within the ONS device, which can be operated as either an optical sensor or a synaptic device depending on applied electrical polarity, provides significant advantages over previous heterogeneous integration methods, particularly regarding material selection, structural compatibility, and device fabrication complexity. The ONS device exhibits distinct wavelength responses essential for emulating colored image recognition capability inherent in the human visual system. Additionally, the seamless integration of electronics and photonics within a unified material system establishes a novel paradigm for optical retrieval, enabling real-time perception of the encoded status of the ONS device. These findings represent substantial advancements in near-sensor computing platforms and open a new horizon for all-inorganic perovskite optoelectronic technologies.

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Publication Details

Journal
Advanced Science
Published
2024-12-16
DOI
https://doi.org/10.1002/advs.202409933
Citations
16
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
3.34

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article

All‐Inorganic Perovskite Quantum‐Dot Optical Neuromorphic Synapses for Near‐Sensor Colored Image Recognition

Yung‐Chi Yao, C. S. Lue, Chia‐Jung Lee, Hongseok Oh et al.
16 citations
Advanced Science
Advanced Memory and Neural Computing
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article

All‐Inorganic Perovskite Quantum‐Dot Optical Neuromorphic Synapses for Near‐Sensor Colored Image Recognition

Yung‐Chi Yao, C. S. Lue, Chia‐Jung Lee, Hongseok Oh, Ya‐Ju Lee, Jinn‐Kong Sheu, Yongjun Chen, Jindi Feng
article en
16 citations

Abstract

As the demand for the neuromorphic vision system in image recognition experiences rapid growth, it is imperative to develop advanced architectures capable of processing perceived data proximal to sensory terminals. This approach aims to reduce data movement between sensory and computing units, minimizing the need for data transfer and conversion at the sensor-processor interface. Here, an optical neuromorphic synaptic (ONS) device is demonstrated by homogeneously integrating optical-sensing and synaptic functionalities into a unified material platform, constructed exclusively by all-inorganic perovskite CsPbBr<sub>3</sub> quantum dots (QDs). The dual functionality of each unit within the ONS device, which can be operated as either an optical sensor or a synaptic device depending on applied electrical polarity, provides significant advantages over previous heterogeneous integration methods, particularly regarding material selection, structural compatibility, and device fabrication complexity. The ONS device exhibits distinct wavelength responses essential for emulating colored image recognition capability inherent in the human visual system. Additionally, the seamless integration of electronics and photonics within a unified material system establishes a novel paradigm for optical retrieval, enabling real-time perception of the encoded status of the ONS device. These findings represent substantial advancements in near-sensor computing platforms and open a new horizon for all-inorganic perovskite optoelectronic technologies.

Advanced ScienceVol. 12(5)
Soongsil University (KR), National Cheng Kung University (TW)
National Cheng Kung University, National Science and Technology Council
Openalex Percentile: Top 7%
Advanced Memory and Neural Computing
3.34
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