An artificial neural network chip based on two-dimensional semiconductor

Ma, Shunli; Wu, Tianxiang; Chen, Xinyu; Wang, Yin; Tang, Hongwei; Yao, Yuting; Wang, Yan; Zhu, Zhaohui; Deng, Jing‐Fa; Wan, Jing; Lü, Ye; Sun, Zhengzong; Xu, Zihan; Riaud, Antoine; Wu, Chenjian; Zhang, David Wei; Chai, Yang; Zhou, Peng; Ren, Junyan; Bao, Wenzhong

doi:10.1016/j.scib.2021.10.005

Cited by 34 publications

(17 citation statements)

References 62 publications

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“…In 2021, Ma et al proposed the first MoS 2 artificial neural network (ANN) chip, which produced hundreds of wafer-level FETs and high-uniformity MoS 2 thin films. They also implemented a top-gate structure FET using a gate-last process [ 20 ]. In 2019, Bian et al combined FET feature analysis and ML algorithms to distinguish five purine compounds.…”

Section: Applications Of Transistor-based Biosensorsmentioning

confidence: 99%

“…As highly sensitive sensor devices, transistor-based biochemical sensors have great potential when combined with artificial intelligence (AI). Using machine learning (ML) methods to design devices and quantitatively analyze biological signals measured by transistor-based biochemical sensors may greatly improve detection accuracy and efficiency [ 20 , 21 , 22 , 23 , 24 ]. These developments will help transistor-based biochemical sensors pave the way for the next generation of point-of-care testing [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Applications of Transistor-Based Biochemical Sensors

Gao

Ming

2023

Biosensors

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Transistor-based biochemical sensors feature easy integration with electronic circuits and non-invasive real-time detection. They have been widely used in intelligent wearable devices, electronic skins, and biological analyses and have shown broad application prospects in intelligent medical detection. Field-effect transistor (FET) sensors have high sensitivity, reasonable specificity, rapid response, and portability and provide unique signal amplification during biochemical detection. Organic field-effect transistor (OFET) sensors are lightweight, flexible, foldable, and biocompatible with wearable devices. Organic electrochemical transistor (OECT) sensors convert biological signals in body fluids into electrical signals for artificial intelligence analysis. In addition to biochemical markers in body fluids, electrophysiology indicators such as electrocardiogram (ECG) signals and body temperature can also cause changes in the current or voltage of transistor-based biochemical sensors. When modified with sensitive substances, sensors can detect specific analytes, improve sensitivity, broaden the detection range, and reduce the limit of detection (LoD). In this review, we introduce three kinds of transistor-based biochemical sensors: FET, OFET, and OECT. We also discuss the fabrication processes for transistor sources, drains, and gates. Furthermore, we demonstrated three sensor types for body fluid biomarkers, electrophysiology signals, and development trends. Transistor-based biochemical sensors exhibit excellent potential in multi-mode intelligent analysis and are good candidates for the next generation of intelligent point-of-care testing (iPOCT).

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Section: Applications Of Transistor-based Biosensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applications of Transistor-Based Biochemical Sensors

Gao

Ming

2023

Biosensors

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“…Nevertheless, most statistical models align with linear regression theory; assuming that there is nonlinear relationship between pollutant concentration and weather conditions, linear regression is difficult to be applied to nonlinear strongly coupled systems [ 31 ]. So far, the artificial intelligence (AI) technique has been extensively applied in a variety of research areas [ 32 , 33 , 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Guo

Wang

2023

Toxics

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Anthropogenic sources of fine particulate matter (PM2.5) threaten ecosystem security, human health and sustainable development. The accuracy prediction of daily PM2.5 concentration can give important information for people to reduce their exposure. Artificial neural networks (ANNs) and wavelet-ANNs (WANNs) are used to predict daily PM2.5 concentration in Shanghai. The PM2.5 concentration in Shanghai from 2014 to 2020 decreased by 39.3%. The serious COVID-19 epidemic had an unprecedented effect on PM2.5 concentration in Shanghai. The PM2.5 concentration during the lockdown in 2020 of Shanghai is significantly reduced compared to the period before the lockdown. First, the correlation analysis is utilized to identify the associations between PM2.5 and meteorological elements in Shanghai. Second, by estimating twelve training algorithms and twenty-one network structures for these models, the results show that the optimal input elements for daily PM2.5 concentration predicting models were the PM2.5 from the 3 previous days and fourteen meteorological elements. Finally, the activation function (tansig-purelin) for ANNs and WANNs in Shanghai is better than others in the training, validation and forecasting stages. Considering the correlation coefficients (R) between the PM2.5 in the next day and the input influence factors, the PM2.5 showed the closest relation with the PM2.5 1 day lag and closer relationships with minimum atmospheric temperature, maximum atmospheric pressure, maximum atmospheric temperature, and PM2.5 2 days lag. When Bayesian regularization (trainbr) was used to train, the ANN and WANN models precisely simulated the daily PM2.5 concentration in Shanghai during the training, calibration and predicting stages. It is emphasized that the WANN1 model obtained optimal predicting results in terms of R (0.9316). These results prove that WANNs are adept in daily PM2.5 concentration prediction because they can identify relationships between the input and output factors. Therefore, our research can offer a theoretical basis for air pollution control.

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“…With the development of artificial intelligence and cloud computing, massive data puts forward higher requirements for low power consumption of computers . However, traditional von Neumann computing architectures limit computation speed due to the separation of storage and computation units and increase power consumption during data transmission . Unlike the von Neumann architecture, the neuromorphic computing can handle transfer and storage of data by a single device that can simulate the synapse functions. , Therefore, neuromorphic computing can break through existing computational bottlenecks by simulating the functions of the human brain with high computational speed and low energy consumption. − This significantly reduces the energy dissipation in the process of data transmission and enhances the ability to process data in parallel. − …”

Section: Introductionmentioning

confidence: 99%

“…1 However, traditional von Neumann computing architectures limit computation speed due to the separation of storage and computation units and increase power consumption during data transmission. 2 Unlike the von Neumann architecture, the neuromorphic computing can handle transfer and storage of data by a single device that can simulate the synapse functions. 3,4 Therefore, neuromorphic computing can break through existing computational bottlenecks by simulating the functions of the human brain with high computational speed and low energy consumption.…”

Section: ■ Introductionmentioning

confidence: 99%

Demonstration of Nonvolatile Storage and Synaptic Functions in All-Two-Dimensional Floating-Gate Transistors Based on MoS₂ Channels

Chen

et al. 2023

ACS Appl. Electron. Mater.

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With the advent of post-Moore era, the development of memory devices based on bulk materials gradually entered the bottleneck period. Two-dimensional (2D) materials have received much attention due to their excellent optoelectronic and mechanical properties. Also, floating-gate devices based on 2D van der Waals heterostructures have drawn widespread attention in virtue of their great potential for nonvolatile memory. In this paper, a floating-gate device based on a MoS 2 /BN/graphene heterostructure was fabricated and its electrical storage performance and synaptic function were investigated. Finally, the device obtains a switching ratio of close to ∼10 5 , a large storage window of 107.8 V under a sweeping range of ±60 V, good endurance after 1000 cycles, and charge retention capability above 1500 s. In addition, the device can be used as an artificial synapse to simulate a basic synaptic function and achieve a more linear and symmetrical longterm potentiation and long-term depression profiles. At the same time, the constructed convolutional neural network using this device reaches a high recognition accuracy of 95.5% for handwritten numerals after 1000 times training. These results demonstrate the great potential of 2D material floating-gate devices for nonvolatile memory and neuromorphic computing, which pave the way for the development of next-generation memory devices.

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An artificial neural network chip based on two-dimensional semiconductor

Cited by 34 publications

References 62 publications

Applications of Transistor-Based Biochemical Sensors

Applications of Transistor-Based Biochemical Sensors

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Demonstration of Nonvolatile Storage and Synaptic Functions in All-Two-Dimensional Floating-Gate Transistors Based on MoS₂ Channels

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An artificial neural network chip based on two-dimensional semiconductor

Cited by 34 publications

References 62 publications

Applications of Transistor-Based Biochemical Sensors

Applications of Transistor-Based Biochemical Sensors

Predicting of Daily PM2.5 Concentration Employing Wavelet Artificial Neural Networks Based on Meteorological Elements in Shanghai, China

Demonstration of Nonvolatile Storage and Synaptic Functions in All-Two-Dimensional Floating-Gate Transistors Based on MoS2 Channels

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Demonstration of Nonvolatile Storage and Synaptic Functions in All-Two-Dimensional Floating-Gate Transistors Based on MoS₂ Channels