Floating-gate Field-effect Transistor Research Articles

Enhancing Electrochemical Sensing through Molecular Engineering of Reduced Graphene Oxide-Solution Interfaces and Remote Floating-Gate FET Analysis.

Two-dimensional nanomaterials such as reduced graphene oxide (rGO) have captured significant attention in the realm of field-effect transistor (FET) sensors due to their inherent high sensitivity and cost-effective manufacturing. Despite their attraction, a comprehensive understanding of rGO-solution interfaces (specifically, electrochemical interfacial properties influenced by linker molecules and surface chemistry) remains challenging, given the limited capability of analytical tools to directly measure intricate solution interface properties. In this study, we introduce an analytical tool designed to directly measure the surface charge density of the rGO-solution interface leveraging the remote floating-gate FET (RFGFET) platform. Our methodology involves characterizing the electrochemical properties of rGO, which are influenced by adhesion layers between SiO2 and rGO, such as (3-aminopropyl)trimethoxysilane (APTMS) and hexamethyldisilazane (HMDS). The hydrophilic nature of APTMS facilitates the acceptance of oxygen-rich rGO, resulting in a noteworthy pH sensitivity of 56.8 mV/pH at the rGO-solution interface. Conversely, hydrophobic HMDS significantly suppresses the pH sensitivity from the rGO-solution interface, attributed to the graphitic carbon-rich surface of rGO. Consequently, the carbon-rich surface facilitates a denser arrangement of 1-pyrenebutyric acid N-hydroxysuccinimide ester linkers for functionalizing capturing probes on rGO, resulting in an enhanced sensitivity of lead ions by 32% in our proof-of-concept test.

Analyzing Various Structural and Temperature Characteristics of Floating Gate Field Effect Transistors Applicable to Fine-Grain Logic-in-Memory Devices.

Although the von Neumann architecture-based computing system has been used for a long time, its limitations in data processing, energy consumption, etc. have led to research on various devices and circuit systems suitable for logic-in-memory (LiM) computing applications. In this paper, we analyze the temperature-dependent device and circuit characteristics of the floating gate field effect transistor (FGFET) source drain barrier (SDB) and FGFET central shallow barrier (CSB) identified in previous papers, and their applicability to LiM applications is specifically confirmed. These FGFETs have the advantage of being much more compatible with existing silicon-based complementary metal oxide semiconductor (CMOS) processes compared to devices using new materials such as ferroelectrics for LiM computing. Utilizing the 32 nm technology node, the leading-edge node where the planar metal oxide semiconductor field effect transistor structure is applied, FGFET devices were analyzed in TCAD, and an environment for analyzing circuits in HSPICE was established. To seamlessly connect FGFET-based devices and circuit analyses, compact models of FGFET-SDB and -CSBs were developed and applied to the design of ternary content-addressable memory (TCAM) and full adder (FA) circuits for LiM. In addition, depression and potential for application of FGFET devices to neural networks were analyzed. The temperature-dependent characteristics of the TCAM and FA circuits with FGFETs were analyzed as an indicator of energy and delay time, and the appropriate number of CSBs should be applied.

Comparing solution-gate and bottom-gate nanowire field-effect transistors on pH sensing with different salt concentrations and surface modifications

Field-effect transistors (FETs) have been developed as pH sensors by using various device structures, fabrication technologies, and sensing film materials. Different transistor structures, like extended-gate (EG) FETs, floating-gate FET sensors, and dual-gate (DG) FETs, can enhance the sensor performance. In this article, we report the effects of using solution-gate and bottom-gate FET configurations on pH sensing and investigate the influence of different ionic concentrations of buffers in the measured signals. The surface charge of hafnium dioxide (HfO2) affected by the buffer pH, with/without the modification of polyethylene glycol (PEG) terminated with hydroxyl groups, and the location of applied gate voltage are vital factors to the sensor performance in pH sensing. Based on the results, the solution-gate FET exhibits good pH sensitivity even in the high ionic strength solutions of bis-tris propane (BTP), and these values of pH sensitivity are close to the Nernst limit (59.2 mV/pH). In general, silane-PEG-OH modification can reduce the deviations of measured signals in pH sensing. The performance of bottom-gate FET is inferior in the BTP buffers with high ionic solutions but suitable to be operated in low ionic concentrations, such as 0.1, 1, and 10 mM BTP buffers. The size of the ions was also studied and discussed. The solution-gate FET demonstrates excellent performance under high ionic strengths, meaning a more significant potential for detecting biological molecules under physiological conditions.

Parameter Extraction of Extended Floating Gate Field-Effect Transistors (EGFETs): Estimating the Threshold Voltage, Series Resistance, and Mobility Degradation From I–V Measurements

Parameter Extraction of Extended Floating Gate Field-Effect Transistors (EGFETs): Estimating the Threshold Voltage, Series Resistance, and Mobility Degradation From <i>I</i>–<i>V</i> Measurements

A large-scale integrated vector–matrix multiplication processor based on monolayer molybdenum disulfide memories

Data-driven algorithms—such as signal processing and artificial neural networks—are required to process and extract meaningful information from the massive amounts of data currently being produced in the world. This processing is, however, limited by the traditional von Neumann architecture with its physical separation of processing and memory, which motivates the development of in-memory computing. Here we report an integrated 32 × 32 vector–matrix multiplier with 1,024 floating-gate field-effect transistors that use monolayer molybdenum disulfide as the channel material. In our wafer-scale fabrication process, we achieve a high yield and low device-to-device variability, which are prerequisites for practical applications. A statistical analysis highlights the potential for multilevel and analogue storage with a single programming pulse, allowing our accelerator to be programmed using an efficient open-loop programming scheme. We also demonstrate reliable, discrete signal processing in a parallel manner.

Two-dimensional materials memory devices with floating metal gate for neuromorphic applications

Emerging technologies such as neuromorphic computing and nonvolatile memories based on floating gate field-effect transistors (FETs) hold promise for addressing a wide range of artificial intelligence tasks. For example, neuromorphic computing seeks to emulate the human brain's functionality and employs a device that mimics the role of a synapse in the brain. However, achieving a high current ON/OFF ratio for the program and erase states of nonvolatile memory and neuromorphic computing device with a metal gate is necessary. This study demonstrates a multi-functional device based on heterostructures of transition metal dichalcogenides (TMDCs) with a metal floating gate. Five different channel materials (SnS2, WSe2, MoS2, WS2, and MoTe2) were employed, and hexagonal boron nitride (h-BN) was used as a tunneling layer. The study found that n-type SnS2 exhibits high endurance (15,000 cycles), good retention (2.4 × 105 s), and the highest current ON/OFF ratio (∼2.58 × 108) among the materials for the program and erase states. Moreover, the SnS2 device exhibits synaptic behavior and offers highly stable operation at room temperature. Furthermore, the device shows high linearity in both potentiation and depression, with good retention time and repeatable results with low cycle-to-cycle variations. Additionally, the study used an artificial neural network (ANN) for MNIST simulation of image recognition and achieved the highest accuracy of ∼92 % based on the SnS2 synaptic device experimental results. These findings pave the way for developing nonvolatile memory devices and their applications in brain-inspired neuromorphic computing and artificial intelligence systems.

Investigation on floating-gate field-effect transistor for logic-in-memory application

In this paper, we present analysis results on the applicability of a previously introduced memory device, floating-gate field-effect transistor (FGFET), to a logic-in-memory (LiM) system for the first time. Device optimization and compact modeling were performed using a well-calibrated technology computer-aided design model and the results of applying LiM circuits were arranged. Device optimization in the 32 nm technology node was conducted by assessing the device performance in terms of memory window, retention time, and write speed. After device optimization, the operational characteristics were analyzed by applying the proposed compact model to a full adder (FA) circuit and a ternary content addressable memory (TCAM) circuit with LiM characteristics. Compared to FA and TCAM circuits composed of conventional FETs, the FGFET-based circuits demonstrated superior performance in terms of area and operating characteristics, implying that they offer significant potential for applications in silicon-based LiM technology.

Wafer-scale floating-gate field effect transistor sensor built on carbon nanotubes film for Ppb-level NO2 detection

NO2 is served as an air pollutant and a specific biomarker for disease diagnosis, which becomes a major detection object in environmental protection and health medical fields. Transition metal dichalcogenides (TMDs) materials have attracted considerable attention for their ability to detect NO2 without heating conditions. The requirements in the above fields for the lower limit of NO2 concentration detection are as low as ppb-level, but the trace gas is difficult to cause the large electrical signal changes, which becomes the bottleneck for TMDS materials in NO2 detection. Moreover, TMDs-based conductance sensors are susceptible to humidity interference at room temperature, which also have challenges in device size, batch fabrication, and reproducibility. How to achieve miniaturization and integration of TMDs-based gas sensors will provide a boost to portable detection of NO2 in environmental protection and health medical fields. Herein, on the basis of the principle of top-gate field effect transistor (FET), a strategy of using gas-sensing materials as the floating-gate (FG) in FET sensor is proposed to realize the detection of trace gas. The channel, dielectric layer, and FG are formed by carbon nanotubes, Y2O3, and Pd/WS2 respectively. The unique amplification effect of carbon-based FET on the gas-sensing signal is utilized to achieve NO2 detection down to 20 ppb. Furthermore, visible-light-assisted detection mode is applied to the FG FET sensor, achieving stable anti-humidity property and 100% recoverability at 10%–70% RH. We expect our work can provide a significant approach to developing micro gas sensor chips for environmental detection and portable medical applications.

Reconfigurable Logic-in-Memory Computing Based on a Polarity-Controllable Two-Dimensional Transistor.

Logic-in-memory architecture holds great promise to meet the high-performance and energy-efficient requirements of data-intensive scenarios. Two-dimensional compacted transistors embedded with logic functions are expected to extend Moore's law toward advanced nodes. Here we demonstrate that a WSe2/h-BN/graphene based middle-floating-gate field-effect transistor can perform under diverse current levels due to the controllable polarity by the control gate, floating gate, and drain voltages. Such electrical tunable characteristics are employed for logic-in-memory architectures and can behave as reconfigurable logic functions of AND/XNOR within a single device. Compared to the conventional devices like floating-gate field-effect transistors, our design can greatly decrease the consumption of transistors. For AND/NAND, it can save 75% transistors by reducing the transistor number from 4 to 1; for XNOR/XOR, it is even up to 87.5% with the number being reduced from 8 to 1.

Carbon Nanotube Field-Effect Transistor Biosensor with an Enlarged Gate Area for Ultra-Sensitive Detection of a Lung Cancer Biomarker.

Carcinoembryonic antigen (CEA) is a recognized biomarker for lung cancer and can be used for early detection. However, the clinical value of CEA is not fully realized due to the rigorous requirement for high-sensitivity and wide-range detection methods. Field-effect transistor (FET) biosensors, as one of the potentially powerful platforms, may detect CEA with a significantly higher sensitivity than conventional clinical testing equipment, while their sensitivity and detection range for CEA are far below the requirement for early detection. Here, we construct a floating gate FET biosensor to detect CEA based on a semiconducting carbon nanotube (CNT) film combined with an undulating yttrium oxide (Y2O3) dielectric layer as the biosensing interface. Utilizing an undulating biosensing interface, the proposed device showed a wider detection range and optimized sensitivity and detection limit, which benefited from an increase of probe-binding sites on the sensing interface and an increase of electric double-layer capacitance, respectively. The outcomes of analytical studies confirm that the undulating Y2O3 provided the desired biosensing surface for probe immobilization and performance optimization of a CNT-FET biosensor toward CEA including a wide detection range from 1 fg/mL to 1 ng/mL, good linearity, and high sensitivity of 72 ag/mL. More crucially, the sensing platform can function normally in the complicated environment of fetal bovine serum, indicating its great promise for early lung cancer screening.

An organ-on-chip device with integrated charge sensors and recording microelectrodes

Continuous monitoring of tissue microphysiology is a key enabling feature of the organ-on-chip (OoC) approach for in vitro drug screening and disease modeling. Integrated sensing units are particularly convenient for microenvironmental monitoring. However, sensitive in vitro and real-time measurements are challenging due to the inherently small size of OoC devices, the characteristics of commonly used materials, and external hardware setups required to support the sensing units. Here we propose a silicon-polymer hybrid OoC device that encompasses transparency and biocompatibility of polymers at the sensing area, and has the inherently superior electrical characteristics and ability to house active electronics of silicon. This multi-modal device includes two sensing units. The first unit consists of a floating-gate field-effect transistor (FG-FET), which is used to monitor changes in pH in the sensing area. The threshold voltage of the FG-FET is regulated by a capacitively-coupled gate and by the changes in charge concentration in close proximity to the extension of the floating gate, which functions as the sensing electrode. The second unit uses the extension of the FG as microelectrode, in order to monitor the action potential of electrically active cells. The layout of the chip and its packaging are compatible with multi-electrode array measurement setups, which are commonly used in electrophysiology labs. The multi-functional sensing is demonstrated by monitoring the growth of induced pluripotent stem cell-derived cortical neurons. Our multi-modal sensor is a milestone in combined monitoring of different, physiologically-relevant parameters on the same device for future OoC platforms.

Non-volatile logic-in-memory ternary content addressable memory circuit with floating gate field effect transistor

Due to the limitations of the currently widely used von Neumann architecture-based computing system, research on various devices and circuit systems suitable for logic-in-memory computing applications has been conducted. In this work, the silicon-based floating gate memory cell transistor structure, which has been attracting attention as a memory to replace the dynamic random access memory or NAND Flash technology, was newly recalled, and its applicability to logic-in-memory application was confirmed. This floating gate field effect transistor (FGFET) has the advantage that the compatibility of the existing silicon-based complementary metal–oxide–semiconductor (CMOS) process is far superior to that of logic-in-memory application devices to which materials with new memory characteristics are applied. At the 32 nm technology node, which is the front node to which the planar MOSFET structure is applied, an analysis environment that can simultaneously analyze the device and circuit of the FGFET was established. For a seamless connection between FGFET-based devices and circuit analysis, the compact model of the FGFET was developed, which is applied to logic-in-memory ternary content addressable memory (TCAM) circuit design. It was verified that the two types of logic-in-memory TCAM circuits to which FGFETs are applied are superior to a conventional CMOS FET-based TCAM circuit in the number of devices used (=circuit area) and power/energy efficiency.

Rapid, Sensitive, Label-Free Electrical Detection of SARS-CoV-2 in Nasal Swab Samples.

Rapid diagnosis of coronavirus disease 2019 (COVID-19) is key for the long-term control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) amid renewed threats of mutated SARS-CoV-2 around the world. Here, we report on an electrical label-free detection of SARS-CoV-2 in nasopharyngeal swab samples directly collected from outpatients or in saliva-relevant conditions by using a remote floating-gate field-effect transistor (RFGFET) with a 2-dimensional reduced graphene oxide (rGO) sensing membrane. RFGFET sensors demonstrate rapid detection (<5 min), a 90.6% accuracy from 8 nasal swab samples measured by 4 different devices for each sample, and a coefficient of variation (CV) < 6%. Also, RFGFET sensors display a limit of detection (LOD) of pseudo-SARS-CoV-2 that is 10 000-fold lower than enzyme-linked immunosorbent assays, with a comparable LOD to that of reverse transcription-polymerase chain reaction (RT-PCR) for patient samples. To achieve this, comprehensive systematic studies were performed regarding interactions between SARS-CoV-2 and spike proteins, neutralizing antibodies, and angiotensin-converting enzyme 2, as either a biomarker (detection target) or a sensing probe (receptor) functionalized on the rGO sensing membrane. Taken together, this work may have an immense effect on positioning FET bioelectronics for rapid SARS-CoV-2 diagnostics.

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

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.

Analysis of Logic-in-Memory Full Adder Circuit With Floating Gate Field Effect Transistor (FGFET)

Analysis of Logic-in-Memory Full Adder Circuit With Floating Gate Field Effect Transistor (FGFET)

Parameter Extraction of Extended Floating Gate Field Effect Transistors (EGFETs): Estimating the Threshold Voltage, Series Resistance, and Mobility Degradation From I-V Measurements

Parameter Extraction of Extended Floating Gate Field Effect Transistors (EGFETs): Estimating the Threshold Voltage, Series Resistance, and Mobility Degradation From I-V Measurements

Macroscopic analysis and design of Si HFGFET gas sensor for sensitive gas detection

In this study, we investigate gas sensing performance of horizontal floating-gate field-effect transistor (HFGFET) gas sensors having different FET channel width and length, and the number and length of FG fingers using both technology computer-aided design (TCAD) device simulation and actual gas sensing measurement. The HFGFET gas sensors have a control-gate (CG) and a FG, which are horizontally interdigitated. An indium oxide (In2O3) film is locally deposited between the CG and FG to be used as a sensing layer. Utilizing a simplified equivalent circuit and electrical characteristics of the HFGFET gas sensors, we define a new parameter G, which represents a magnitude of gas sensitivity. The effect of channel width and length, and the number and length of FG fingers of the HFGFET gas sensors on gas sensitivity is first investigated by TCAD device simulation, and then verified by NO2 and H2S gas sensing measurement.

A 5.67 ENOB Vector Matrix Multiplier with Charge Storage FET Cells and Non-Linearity Compensation Techniques

In this paper, we provide a thorough analysis and enhancement techniques of the linearity between the input voltage and output current in charge storage field effect transistor (FET) cells for a vector–matrix multiplier array in neural networks. A planar floating gate FET cell revealed superior linearity, because of boosting the floating gate using a drain voltage through capacitive coupling. If the coupling capacitance is extended by up to half of the gate capacitance, the coefficient of determination for linear regression is easily greater than 99.5%. However, the linearity of the charge trap FET, which keeps electrons in the insulating gate dielectric, must be compensated by either boosting the drain voltage, using a non-linear input driver, or supplying a quadratic current through an auxiliary path in the cell. Drain voltage boosting is limitedly effective over a small input range, while the auxiliary current path shows a coefficient of determination greater than 99.5% over a 500 mV input range. If the cell area matters, the charge trap FET with a diode connected FET as an auxiliary current path revealed the best performance, with an effective number of bits of 5.67, in a 21.3 F2 cell area.

Remote Floating-Gate Field-Effect Transistor with 2-Dimensional Reduced Graphene Oxide Sensing Layer for Reliable Detection of SARS-CoV-2 Spike Proteins.

Despite intensive research of nanomaterials-based field-effect transistors (FETs) as a rapid diagnostic tool, it remains to be seen for FET sensors to be used for clinical applications due to a lack of stability, reliability, reproducibility, and scalability for mass production. Herein, we propose a remote floating-gate (RFG) FET configuration to eliminate device-to-device variations of two-dimensional reduced graphene oxide (rGO) sensing surfaces and most of the instability at the solution interface. Also, critical mechanistic factors behind the electrochemical instability of rGO such as severe drift and hysteresis were identified through extensive studies on rGO-solution interfaces varied by rGO thickness, coverage, and reduction temperature. rGO surfaces in our RFGFET structure displayed a Nernstian response of 54 mV/pH (from pH 2 to 11) with a 90% yield (9 samples out of total 10), coefficient of variation (CV) < 3%, and a low drift rate of 2%, all of which were calculated from the absolute measurement values. As proof-of-concept, we demonstrated highly reliable, reproducible, and label-free detection of spike proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a saliva-relevant media with concentrations ranging from 500 fg/mL to 5 μg/mL, with an R2 value of 0.984 and CV < 3%, and a guaranteed limit of detection at a few pg/mL. Taken together, this new platform may have an immense effect on positioning FET bioelectronics in a clinical setting for detecting SARS-CoV-2.

Multifunctional Logic‐in‐Memory Cell Based on Wafer‐Scale MoS2 Thin Films Prepared by Atomic Layer Deposition

Large‐volume data storage and high‐efficiency data processing have been greatly urged by the fast development in artificial intelligence and internet of things involving novel semiconductor materials. Logic‐in‐memory has provided a promising route to address the processing/storage bottleneck in advanced computing systems. Herein, a logic‐in‐memory design based on wafer‐scale MoS2 floating‐gate field‐effect transistor (FGFET) arrays is demonstrated. Multiple logic states and over 10 V memory window are achieved through electrical gate modulation, which further enables in‐memory computing including logic functions of inverter and two‐input NAND gates. Such robust and stable logic‐in‐memory is ascribed to the highly uniform wafer‐scale MoS2 thin film prepared by atomic layer deposition (ALD)‐based synthesis and the reproducible electrical performance of the device arrays. As both memory and computing units are based on floating‐gate FET architecture with area efficiency, the proposed logic‐in‐memory cell can accomplish multifunctional tasks with fewer transistors, suggesting a potential application in high‐density and complex information processing and data storage systems.