Synergistic improvement of sensing performance in ferroelectric transistor gas sensors using remnant polarization

Bae, Jong-Ho; Lee, Jung‐Kyu; Hong, Seongbin; Kim, Jaehyeon; Jeong, Yujeong; Kwon, Dongseok; Koo, Ryun-Han; Jung, Gyuweon; Han, Changhyeon; Kim, Jeong‐Han; Park, Byung‐Gook; Kwon, Daewoong; Shin, Wonjun

doi:10.1039/d2mh00340f

Cited by 14 publications

(7 citation statements)

References 41 publications

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“…Failure has been attributed to defects in the ferroelectric and dielectric layers, including oxygen vacancies, hydrogen, carbon, nitrogen atoms, and grain boundaries. [29][30][31] Additionally, these defects are known to generate 1/f noise. [31] In semiconductor devices, carrier trapping/detrapping processes and mobility fluctuation are known to generate low-frequency noise (LFN) such as 1/f noise and random telegraph noise whose magnitude far exceeds that of white noise such as thermal noise and shot noise.…”

Section: Doi: 101002/aisy202200377mentioning

confidence: 99%

1/f Noise in Synaptic Ferroelectric Tunnel Junction: Impact on Convolutional Neural Network

Shin

Min

Bae

et al. 2023

Advanced Intelligent Systems

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Conventional von Neumann architecture employs a physically distinct processor and memory. This results in considerable delay and energy costs, which is referred to as the von Neumann bottleneck. [1][2][3] Additionally, the memory wall, that is, the performance difference between memory and processing units, is continuously increasing and has become a key concern in the Internet of Things era. Therefore, a novel method of in-memory computing called neuromorphic computing was introduced, which allows concurrent logic and memory operations. [4][5][6] By combining logic and memory functionalities, neuromorphic computing can prevent frequent, time-consuming data transfers and thus significantly reduce power consumption.Neuromorphic computing requires synaptic devices with low-power consumption, multilevel conductance, high switching speeds, and small sizes. [7][8][9][10][11][12][13][14][15][16] In this regard, memristor devices, whose resistance states can be modified using electrical voltages, are promising candidates for synaptic devices, including resistive memristors, [7][8][9][10] magnetic tunnel junctions, [11][12][13] and phase-change memristors. [14][15][16] Recently, ferroelectric tunnel junctions (FTJs) have attracted considerable research attention as candidates for memristor devices in neuromorphic computing. [17][18][19] Under an applied voltage, the polarization of collectively ordered electrical dipoles in ferroelectric materials can be switched, resulting in a change in tunnel electroresistance (TER). Since the discovery of ferroelectricity in HfO 2 -based thin films in 2011, FTJs employing HfO 2 -based ferroelectric materials have piqued the interest of researchers owing to their compatibility with CMOS technologies. [20] In addition, FTJs have advantages of low-power operation, high access speeds, excellent scalability, and good durability. Accordingly, extensive efforts have been directed toward improving the performance of FTJs. The structure of an FTJ has evolved from metal-ferroelectric-metal to metal-ferroelectric-insulator-metal and metal-ferroelectric-insulator-semiconductor (MFIS) stacks as the asymmetric barrier potential profile between the top and bottom electrodes increases the TER ratio. [21,22] Numerous studies have indicated that the remnant polarization in FTJs can be improved by optimizing the fabrication parameters, such as the rapid thermal annealing (RTA) temperature, [23] thickness of the

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Section: Doi: 101002/aisy202200377mentioning

confidence: 99%

1/f Noise in Synaptic Ferroelectric Tunnel Junction: Impact on Convolutional Neural Network

Shin

Min

Bae

et al. 2023

Advanced Intelligent Systems

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“…Using Cooley and Tukey's Fast Fourier Transform (FFT), the DFT can be divided into two parts: [46] (11) The first and second parts of Equation 11 represent the even part (x 2m ) and odd part (x 2m + 1 ) of the input vector, respectively. By separating N size DFT into N/2 size of even part (X even, N/2 ) and the odd part (X odd, N/2 ), we can calculate DFT with size N (O(N 2 )) from two DFTs with size N/2 (O(N/2 2 )).…”

Section: In-memory-computed Fft For An Area-and Energy-efficient Gas ...mentioning

confidence: 99%

“…[8,9] Reducible metal oxide-based gas sensors are the most viable choice for the actuation of a reliable gas sensing system due to their large response to various gases, simple fabrication method, ease of operation, and low fabrication cost. [10][11][12] Accordingly, there have been extensive studies to improve the sensing capabilities of metal oxide-based gas sensors. Because it is crucial to detect a low concentration of gas stably in many applications, studies to improve a limit-ofdetection (LOD) of sensors to the target gas have been intensively conducted in both academia and industry.…”

Section: Introductionmentioning

confidence: 99%

In‐Memory‐Computed Low‐Frequency Noise Spectroscopy for Selective Gas Detection Using a Reducible Metal Oxide

et al. 2023

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Concerns about indoor and outdoor air quality, industrial gas leaks, and medical diagnostics are driving the demand for high-performance gas sensors. Owing to their structural variety and large surface area, reducible metal oxides hold great promise for constructing a gas-sensing system. While many earlier reports have successfully obtained a sufficient response to various types of target gases, the selective detection of target gases remains challenging. In this work, a novel method, low-frequency noise (LFN) spectroscopy is presented, to achieve selective detection using a single FET-type gas sensor. The LFN of the sensor is accurately modeled by considering the charge fluctuation in both the sensing material and the FET channel. Exposure to different target gases produces distinct corner frequencies of the power spectral density that can be used to achieve selective detection. In addition, a 3D vertical-NAND flash array is used with the fast Fourier transform method via in-memory-computing, significantly improving the area and power efficiency rate. The proposed system provides a novel and efficient method capable of selectively detecting a target gas using in-memory-computed LFN spectroscopy and thus paving the way for the further development in gas sensing systems.

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“…From conventional uses such as indoor/outdoor air quality monitoring, applications of gas sensors have expanded to include medical diagnosis and food quality control [1][2][3][4][5]. Among different types of gas sensors, metal oxide (MOX)-based gas sensors have merits in terms of the simple fabrication process, low cost, simplicity in use, and a large number of detectable gases [6,7]. Despite the numerous advantages, MOX-based gas sensors still suffer from poor stability and reliability due to their susceptibility to temperature and humidity [8].…”

Section: Introductionmentioning

confidence: 99%

Recovery of off-state stress-induced damage in FET-type gas sensor using self-curing method

et al. 2023

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The need for high-performance gas sensors is driven by concerns over indoor and outdoor air quality, and industrial gas leaks. Due to their structural diversity, vast surface area, and geometric tunability, metal oxides show significant potential for the development of gas sensing systems. Despite the fact that several previous reports have successfully acquired a suitable response to various types of target gases, it remains difficult to maintain the reliability of metal oxide-based gas sensors. In particular, the degradation of the sensor platform under repetitive operation, such as off-state stress (OSS) causes significant reliability issues. We investigate the impact of OSS on the gas sensing performances, including response, low-frequency noise, and signal-to-noise ratio of horizontal floating-gate field-effect-transistor (FET)-type gas sensors. The 1/f noise is increased after the OSS is applied to the sensor because the gate oxide is damaged by hot holes. Therefore, the SNR of the sensor is degraded by the OSS. We applied a self-curing method based on a PN-junction forward current at the body–drain junction to repair the damaged gate oxide and improve the reliability of the sensor. It has been demonstrated that the SNR degradation caused by the OSS can be successfully recovered by the self-curing method.

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Synergistic improvement of sensing performance in ferroelectric transistor gas sensors using remnant polarization

Abstract: Gaseous pollutants, including nitrogen oxides, pose a severe threat to ecosystems and human health; therefore, developing reliable gas-sensing systems to detect them is becoming increasingly important. Among the various options,...

Cited by 14 publications

References 41 publications

1/f Noise in Synaptic Ferroelectric Tunnel Junction: Impact on Convolutional Neural Network

1/f Noise in Synaptic Ferroelectric Tunnel Junction: Impact on Convolutional Neural Network

In‐Memory‐Computed Low‐Frequency Noise Spectroscopy for Selective Gas Detection Using a Reducible Metal Oxide

Recovery of off-state stress-induced damage in FET-type gas sensor using self-curing method

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