Artificial Tactile Sensing System with Photoelectric Output for High Accuracy Haptic Texture Recognition and Parallel Information Processing

Shan, Liuting; Zeng, Huaan; Liu, Yaqian; Zhang, Xianghong; Li, Enlong; Yu, Rengjian; Hu, Yuanyuan; Guo, Tailiang; Chen, Huipeng

doi:10.1021/acs.nanolett.2c02995

Cited by 27 publications

(14 citation statements)

References 51 publications

(64 reference statements)

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“…Figure c shows the fluorescence mapping of a Si QDs/MoS 2 synaptic device, indicating that the fluorescence may facilitate device visualization. Compared with previous synaptic devices with light emission enabled by light-emitting diode (LED) − or light-emitting transistor (LET) structures, , the Si QDs/MoS 2 synaptic device has a simple structure without hole/electron transport layers. Moreover, instead of being triggered by the electrical inputs, the optically stimulated synaptic plasticity and synchronous fluorescence render advantages such as wide bandwidth, negligible RC (R = resistance, C = capacitance) delay and power loss, and global regulation of multiple synaptic devices …”

Section: Resultsmentioning

confidence: 99%

Optogenetics-Inspired Fluorescent Synaptic Devices with Nonvolatility

Wang

et al. 2023

ACS Nano

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Given the synergy of optogenetics and bioimaging in neuroscience, it is possible for light to simultaneously modulate and visualize synaptic events of optoelectronic synaptic devices, which are building blocks of a neuromorphic computing system with optoelectronic integration. Here we demonstrate the realization of the simultaneous modulation and visualization of synaptic events by using optically stimulated synaptic devices based on the heterostructure of fluorescent silicon quantum dots (Si QDs) and monolayer molybdenum disulfide (MoS 2 ). The charge-transferenabled photogating effect of the Si QDs/MoS 2 heterostructure leads to the nonvolatility of the synaptic devices, which exhibit important synaptic functionalities and synchronous fluorescence upon optical stimulation. An array of the Si QDs/MoS 2 optoelectronic synaptic devices is well-employed to mimic robust neural population coding. Defective devices in this array may be pinpointed by the absence of their fluorescence. This work has an important implication for the development of synaptic devices facilitating the system-level diagnosis and device-level positioning of a neuromorphic computing system.

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Section: Resultsmentioning

confidence: 99%

Optogenetics-Inspired Fluorescent Synaptic Devices with Nonvolatility

Wang

et al. 2023

ACS Nano

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“…Except for exploiting the optoelectronic properties of materials for constructing a memristor with light programming as the third virtual terminal, more and more research teams have committed to adjust the mechanical energy of the memristors by external pressures in recent years. [ 107 ] Through physical mechanisms such as the piezoresistive effect, piezoelectric effect, and triboelectric effect, adding force as the third virtual input terminal variable of the conventional two‐terminal memristor affects the physical/electrical characteristics of the devices. [ 108 ] Pressure‐tunable memristors are capable of more sophisticated memory and learning capabilities than regular pressure sensors, which can directly transform external pressure into processed electrical signals.…”

Section: Virtual Multiterminal Memristormentioning

confidence: 99%

Recent Progress in Multiterminal Memristors for Neuromorphic Applications

Leng

Zhang

et al. 2023

Adv Elect Materials

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Neumann system is a conventional computing architecture with divided processor and memory unit which executes computational tasks sequentially which has served as a pillar of contemporary computing since 1945. However, the frequent data shuffling between the separated processor and memory unit induce massive power consumption and latency which is so-called von Neumann bottleneck. [7,8] The human brain is capable of concurrently executing several complicated tasks with enormous parallelism with extremely low power consumption, outstanding fault tolerance, and remarkable durability owing to its extensive connection, functional organizational hierarchy, advanced learning rules, and neuronal plasticity. Inspiring from it, Mead first pioneered the notion of "neuromorphic computing" in the late 1980s and early 1990s. [9,10] Accordingly, to eliminate inherent constraint of the von Neumann systems, substantial effort has been focused on investigating neuromorphic computing systems. [11][12][13] Biological neuromorphic systems consisting 10 11 neurons interconnected with each other via 10 15 synapses [14,15] is capable to respond to environment and history at different levels from simple molecular (nucleic acids could displays adaptive behaviors including self-repair and replication, under stimuli from the local environment), the elementary information-processing blocks in biological systems (neurons could exhibit more than 20 different dynamic behaviors triggered by historically and environmentally electrochemical stimulation), to whole functional systems with more hierarchical complexity (extremely low or high relative humidity (RH), has a substantial impact on the accuracy of human visual system). [16][17][18] Recently, inspired by human sensory processing and perceptual learning, neuromorphic sensing and computing systems incorporating sensors and machine learning algorithms have been demonstrated to perceive, process, and integrate diverse sensory information where the adaptation and learning can be obtained through dynamically updating the weights of neural network according to the different training algorithms. [19,20] However, on contrary to the distributed processing in biological hierarchical architectures which is more adaptable and cognitive for the optimum analysis of complicated information, the modern computing systems adopting centralized processing are still based on static elements with zeroth-order complexity (e.g., transistor). The essential step for developing neuromorphic systems is to The essential step for developing neuromorphic systems is to construct more biorealistic elementary devices with rich spatiotemporal dynamics to exhibit highly separable responses in dynamic environmental circumstances. Unlike transistor-based devices and circuits with zeroth-order complexity, memristors intrinsically express some simple biomimetic functions. However, with only two-terminal structure, precise control of operation principles to ensure large dynamic space, improved linearity and symmetry, multimodal oper...

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“…Tactile perception can be performed through this system, and during the experiment of recognition of 16 fibres, there was a 94.1% accuracy rate. Other than that, this system realized multitask of photoelectric signals accurately and efficiently [12]. Bewley et al developed an optical sensor that could detect and measure a certain object.…”

Section: Photoelectric/optical-base Sensormentioning

confidence: 99%

A review of tactile sensors used in surgical robots

Dong

Huo²

2023

Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022)

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Applications of robotics-assist surgeries developed rapidly in worldwide biomedical research. Haptic and force feedback is one of the main challenges in current research on surgical robotics. Lack of proper haptic and force feedback in roboticassist surgeries will limit the movement and affect the doctors' judgement. Force sensors play an essential role when collecting and transporting feedback signals, and different sensors have their respective advantages and disadvantages. Based on the haptic and force feedback background related to surgical robots, this article will introduce the main features of different force sensors, including but not limited to traditional sensors like optical-based and recent-developed on-skin sensors. This article will also discuss further how the precision and stability of haptic feedback could be improved and the possible applications of new haptic and force feedback technologies in future studies.

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Artificial Tactile Sensing System with Photoelectric Output for High Accuracy Haptic Texture Recognition and Parallel Information Processing

Cited by 27 publications

References 51 publications

Optogenetics-Inspired Fluorescent Synaptic Devices with Nonvolatility

Optogenetics-Inspired Fluorescent Synaptic Devices with Nonvolatility

Recent Progress in Multiterminal Memristors for Neuromorphic Applications

A review of tactile sensors used in surgical robots

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