A Temperature Sensor With a Thermillator

Lee, Mun‐Woo; Han, Joon‐Kyu; Yun, Gyeong‐Jun; Yu, Ji‐Man; Lee, Geon‐Beom; Han, Seungjae; Choi, Yang‐Kyu

doi:10.1109/led.2021.3111622

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…With regard to 1T‐neurons, changes of the electron‐hole pair generation rate and thermionic emission can alter the STL characteristics, which determine the neuronal characteristics. [ 92 ] The properties of memristor neurons and phase‐change neurons can also be changed by the external temperature because the switching mechanism is highly relevant with regard to the thermal dynamics. [ 93 ] Thermal instability can also be problematic in magnetic neurons because the spin polarization ratio of an MTJ decreases when the temperature increases.…”

Section: Future Perspectivementioning

confidence: 99%

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

Han

Yun

Lee

et al. 2022

Adv Funct Materials

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A spiking neural network (SNN) inspired by the structure and principles of the human brain can significantly enhance the energy efficiency of artificial intelligence computing by overcoming the bottlenecks of the conventional von Neumann architecture with its massive parallelism and spike transmissions. The construction of artificial neurons is important for the hardware implementation of an SNN, which generates spike signals when enough synaptic signals are gathered. Because circuit‐level artificial neurons with comparator and reset circuits require considerable hardware area, intensive efforts are devoted in recent years for building artificial neurons at the device level for better area efficiency. Furthermore, artificial sensory neuron devices, which perform neural processing and sensing concurrently, have recently been developed in order to reduce the hardware cost and energy consumption of traditional sensory systems through in‐sensor computing. This review article surveys and benchmarks the recent progress of artificial neuron devices for neural processing and sensing. First, various artificial neuron devices are summarized, including single‐transistor neurons (1T‐neurons), memristor neurons, phase‐change neurons, magnetic neurons, and ferroelectric neurons. Next, cointegration technologies with artificial synaptic devices and artificial sensory neurons for in‐sensor computing are introduced. Finally, the challenges and prospects for developing artificial neuron devices are discussed.

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Section: Future Perspectivementioning

confidence: 99%

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

Han

Yun

Lee

et al. 2022

Adv Funct Materials

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“…When a constant current is applied to the drain as an input, a spike-shaped voltage is iteratively produced as an output from the same drain electrode through a repeated charging and discharging process reported in previous works ( 33 – 35 , 39 , 40 ). The abovementioned process, from charging to discharging, is illustrated with energy band diagrams for steps (1) to (4) in Fig.…”

Section: Resultsmentioning

confidence: 99%

Cryptographic transistor for true random number generator with low power consumption

Kim,

You,

Kim

et al. 2024

Sci. Adv.

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We design a cryptographic transistor (cryptoristor)–based true random number generator (tRNG) with low power consumption and small footprint. This is the first attempt to use irregular and unpredictable operation-induced randomness of a cryptoristor as an entropy source. To extract discrete random numbers with a binary code from the cryptoristor, we developed a noise-coupling analog-to-digital converter. This converter not only converts analog signals to digital random bits but also improves the randomness of the entropy source with low power consumption. The randomness of the cryptoristor is attributed to the random carrier multiplications with the creation and stochastic carrier escape as destruction, which occurs iteratively as long as the input current is fed. The cryptoristor-based tRNG passed 15 randomness test suites of NIST Special Publication 800-22. It is robust to iterative operational stresses and to ambient temperature changes, making it an attractive option for hardware-based security solutions in the Internet of Things due to its low power consumption and small size.

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“…8 However, to translate signals into human-identifiable data, these temperature sensors often require bulky equipment or wired connections and cannot respond immediately to direct external stimuli, restricting their application in portable and real-time detection, especially at harsh external environments. 9,10 The inability to respond instantly to direct stimuli restricts their application in portable and real-time detection, especially at harsh external environments. With the rapid development of artificial intelligence and biomedicine, integrating some smart functions into a temperature sensor would be more favorable and practical.…”

Section: Introductionmentioning

confidence: 99%

“…Temperature is an important measurement parameter in many fields, including agricultural planting, mechanical engineering, human health, and electronic information. − Temperature sensors that can quickly determine the temperature are essential for obtaining critical information on measured objects. , Nowadays, most reported works have only focused on developing high-performance temperature sensors with higher sensitivity, faster response times, and higher repeatability . However, to translate signals into human-identifiable data, these temperature sensors often require bulky equipment or wired connections and cannot respond immediately to direct external stimuli, restricting their application in portable and real-time detection, especially at harsh external environments. , The inability to respond instantly to direct stimuli restricts their application in portable and real-time detection, especially at harsh external environments. With the rapid development of artificial intelligence and biomedicine, integrating some smart functions into a temperature sensor would be more favorable and practical.…”

Section: Introductionmentioning

confidence: 99%

Temperature Sensors Integrated with an Electrochromic Readout toward Visual Detection

Li,

Zhen,

Wang

et al. 2023

ACS Appl. Mater. Interfaces

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Temperature sensors have attracted great attention for personal health care and disease diagnosis in recent years. However, it is still a great challenge to fabricate reliable and highly sensitive temperature sensors that can convert physiological signals into easily readable signals in a convenient way. Herein, an integrated smart temperature sensor system based on a traditional temperature sensor and electrochromic display is proposed for real-time visual detection of temperature. Significantly, a voltage-regulated electrochromic device (ECD) based on tungsten oxide (WO3) and polyaniline (PANI) as the real-time visualization window was integrated into the platform to provide feedback on the temperature change. The ECD would change its color from green to blue based on the electrical signal of the temperature sensor, resulting in a visualized readout that can be monitored through our naked eye. Additionally, the smart temperature sensor system possesses an extremely durable property and cycle stability, remaining around 90% of the initial value even after 15,000 s continuous cycle. Thus, the novel design and low power consumption advantages make it a good candidate to pave the way for developing interactive wearable electronics and intelligent robots as real-time temperature feedback systems.

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A Temperature Sensor With a Thermillator

Cited by 9 publications

References 15 publications

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

A Review of Artificial Spiking Neuron Devices for Neural Processing and Sensing

Cryptographic transistor for true random number generator with low power consumption

Temperature Sensors Integrated with an Electrochromic Readout toward Visual Detection

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