scholarly journals  Progress in the Triboelectric Human–Machine Interfaces (HMIs)-Moving from Smart Gloves to AI/Haptic Enabled HMI in the 5G/IoT Era

2021 ◽  
Vol 1 (1) ◽  
pp. 81-120
Author(s):  
Zhongda Sun ◽  
Minglu Zhu ◽  
Chengkuo Lee
Keyword(s):  
Smart Home ◽  
Haptic Feedback ◽  
Sensory Information ◽  
Complementary Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Measurement Unit ◽  
Triboelectric Nanogenerator ◽  
Wearable Robot ◽  
Human Machine Interfaces ◽  
Self Powered

Entering the 5G and internet of things (IoT) era, human–machine interfaces (HMIs) capable of providing humans with more intuitive interaction with the digitalized world have experienced a flourishing development in the past few years. Although the advanced sensing techniques based on complementary metal-oxide-semiconductor (CMOS) or microelectromechanical system (MEMS) solutions, e.g., camera, microphone, inertial measurement unit (IMU), etc., and flexible solutions, e.g., stretchable conductor, optical fiber, etc., have been widely utilized as sensing components for wearable/non-wearable HMIs development, the relatively high-power consumption of these sensors remains a concern, especially for wearable/portable scenarios. Recent progress on triboelectric nanogenerator (TENG) self-powered sensors provides a new possibility for realizing low-power/self-sustainable HMIs by directly converting biomechanical energies into valuable sensory information. Leveraging the advantages of wide material choices and diversified structural design, TENGs have been successfully developed into various forms of HMIs, including glove, glasses, touchpad, exoskeleton, electronic skin, etc., for sundry applications, e.g., collaborative operation, personal healthcare, robot perception, smart home, etc. With the evolving artificial intelligence (AI) and haptic feedback technologies, more advanced HMIs could be realized towards intelligent and immersive human–machine interactions. Hence, in this review, we systematically introduce the current TENG HMIs in the aspects of different application scenarios, i.e., wearable, robot-related and smart home, and prospective future development enabled by the AI/haptic-feedback technology. Discussion on implementing self-sustainable/zero-power/passive HMIs in this 5G/IoT era and our perspectives are also provided.

scholarly journals Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations

Materials ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 2745 ◽  
Author(s):  
Luis Camuñas-Mesa ◽  
Bernabé Linares-Barranco ◽  
Teresa Serrano-Gotarredona
Keyword(s):  
Neural Networks ◽  
Learning Strategies ◽  
Large Scale ◽  
Sensory Information ◽  
Complementary Metal Oxide Semiconductor ◽  
Cmos Technology ◽  
Oxide Semiconductor ◽  
Hardware Implementations ◽  
Learning Capabilities ◽  
Promising Solution

Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applications related to sensory information processing. These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. The recent development of nanoscale memristors that can be integrated with Complementary Metal–Oxide–Semiconductor (CMOS) technology opens a very promising solution to emulate the behavior of biological synapses. Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems.

scholarly journals Self-Powered Room-Temperature Ethanol Sensor Based on Brush-Shaped Triboelectric Nanogenerator

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jingwen Tian ◽  
Fan Wang ◽  
Yafei Ding ◽  
Rui Lei ◽  
Yuxiang Shi ◽  
...  
Keyword(s):  
Room Temperature ◽  
Liquid Crystal Display ◽  
Characteristic Curve ◽  
Oxide Semiconductor ◽  
Triboelectric Nanogenerator ◽  
Concentration Change ◽  
Resistance Change ◽  
Industrial Monitoring ◽  
Application Potential ◽  
Self Powered

Highly sensitive ethanol sensors have been widely utilized in environmental protection, industrial monitoring, and drink-driving tests. In this work, a fully self-powered ethanol detector operating at room temperature has been developed based on a triboelectric nanogenerator (TENG). The gas-sensitive oxide semiconductor is selected as the sensory component for the ethanol detection, while the resistance change of the oxide semiconductor can well match the “linear” region of the load characteristic curve of TENG. Hence, the output signal of TENG can directly reveal the concentration change of ethanol gas. An accelerator gearbox is applied to support the operation of the TENG, and the concentration change of ethanol gas can be visualized on the Liquid Crystal Display. This fully self-powered ethanol detector has excellent durability, low fabrication cost, and high selectivity of 5 ppm. Therefore, the ethanol detector based on TENG not only provides a different approach for the gas detection but also further demonstrates the application potential of TENG for various sensory devices.

A double-helix-structured triboelectric nanogenerator enhanced with positive charge traps for self-powered temperature sensing and smart-home control systems

Nanoscale ◽  
2018 ◽  
Vol 10 (42) ◽  
pp. 19781-19790 ◽  
Author(s):  
Lingxiao Gao ◽  
Donglin Hu ◽  
Mengke Qi ◽  
Jia Gong ◽  
Hong Zhou ◽  
...  
Keyword(s):  
Control Systems ◽  
Smart Home ◽  
Positive Charge ◽  
Double Helix ◽  
Temperature Sensing ◽  
Triboelectric Nanogenerator ◽  
Self Powered ◽  
Triboelectric Nanogenerators

Triboelectric nanogenerators (TENGs) have been in spotlight for their excellent capability to drive miniature electronics.

scholarly journals Memristor-CMOS Hybrid Circuit for Temporal-Pooling of Sensory and Hippocampal Responses of Cortical Neurons

Materials ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 875 ◽  
Author(s):  
Tien Nguyen ◽  
Khoa Pham ◽  
Kyeong-Sik Min
Keyword(s):  
Cortical Neurons ◽  
Hebbian Learning ◽  
Circuit Simulation ◽  
Sensory Information ◽  
Complementary Metal Oxide Semiconductor ◽  
External Noise ◽  
Oxide Semiconductor ◽  
Output Layer ◽  
Input Layer ◽  
Hybrid Circuit

As a software framework, Hierarchical Temporal Memory (HTM) has been developed to perform the brain’s neocortical functions, such as spatial and temporal pooling. However, it should be realized with hardware not software not only to mimic the neocortical function but also to exploit its architectural benefit. To do so, we propose a new memristor-CMOS (Complementary Metal-Oxide-Semiconductor) hybrid circuit of temporal-pooling here, which is composed of the input-layer and output-layer neurons mimicking the neocortex. In the hybrid circuit, the input-layer neurons have the proximal and basal/distal dendrites to combine sensory information with the temporal/location information from the brain’s hippocampus. Using the same crossbar architecture, the output-layer neurons can perform a prediction by integrating the temporal information on the basal/distal dendrites. For training the proposed circuit, we used only simple Hebbian learning, not the complicated backpropagation algorithm. Due to the simple hardware of Hebbian learning, the proposed hybrid circuit can be very suitable to online learning. The proposed memristor-CMOS hybrid circuit has been verified by the circuit simulation using the real memristor model. The proposed circuit has been verified to predict both the ordinal and out-of-order sequences. In addition, the proposed circuit has been tested with the external noise and memristance variation.

scholarly journals An E-Band 21-dB Variable-Gain Amplifier with 0.5-V Supply in 40-nm CMOS

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 804
Author(s):  
Gibeom Shin ◽  
Kyunghwan Kim ◽  
Kangseop Lee ◽  
Hyun-Hak Jeong ◽  
Ho-Jin Song
Keyword(s):  
Power Consumption ◽  
Frequency Band ◽  
Noise Figure ◽  
Complementary Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Operating Frequency ◽  
Variable Gain Amplifier ◽  
Variable Gain ◽  
Operating Frequency Band ◽  
Shunt Capacitors

This paper presents a variable-gain amplifier (VGA) in the 68–78 GHz range. To reduce DC power consumption, the drain voltage was set to 0.5 V with competitive performance in the gain and the noise figure. High-Q shunt capacitors were employed at the gate terminal of the core transistors to move input matching points for easy matching with a compact transformer. The four stages amplifier fabricated in 40-nm bulk complementary metal oxide semiconductor (CMOS) showed a peak gain of 24.5 dB at 71.3 GHz and 3‑dB bandwidth of more than 10 GHz in 68–78 GHz range with approximately 4.8-mW power consumption per stage. Gate-bias control of the second stage in which feedback capacitances were neutralized with cross-coupled capacitors allowed us to vary the gain by around 21 dB in the operating frequency band. The noise figure was estimated to be better than 5.9 dB in the operating frequency band from the full electromagnetic (EM) simulation.

Triboelectric nanogenerator based self-powered sensor for artificial intelligence

Nano Energy ◽  
2021 ◽  
Vol 84 ◽  
pp. 105887
Author(s):  
Yuankai Zhou ◽  
Maoliang Shen ◽  
Xin Cui ◽  
Yicheng Shao ◽  
Lijie Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Triboelectric Nanogenerator ◽  
Self Powered
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