This study presents a comprehensive statistical reliability analysis of monolithic Complementary Field Effect Transistors (CFETs), focusing on the impact of process-induced variations. The analysis highlights key sources of variability, including Metal Gate Granularity (MGG), Poly Grain Granularity (PGG), Random Dopant Fluctuation (RDF), and Line Edge Roughness (LER). In smaller geometries, the gate metal (or poly-gate) grain granularities play a significant role in threshold voltage (V-th) variations, raising severe reliability concerns. Through calibrated statistical TCAD simulations, the ON current (I-ON) variation of 2.82 & micro;A (2.85 & micro;A) is observed in the nFET (pFET) side of CFET, due to MGG for experimentally reported 5 nm grain size. In advanced technologies like high bandwidth memory (HBM), the gate is made of polysilicon that suffers from the PGG in sub-3 nm node devices. The observed coefficient-ofvariance (CV) of pFET (2.19) is higher than nFET (1.45), which shows higher variability in pFET due to poly grain size variations. Further, the process-induced RDF and LER significantly alter the V-th. Random numbers are generated by doping atoms in the Voronoi volume of each vertex to quantify the undoped and doped channels to statistically analyze the RDF. In contrast to a doped channel with sigma V-th of 9 mV (7 mV) for nFET (pFET), the undoped scenario yields a default standard deviation (sigma V-th) of 2 mV. To characterize LER, correlation length (Lambda) is a prime factor; as Lambda approaches the sheet width, CV increases by 24.32% (37.22%) in nFET (pFET). Thus, the results underscore the need for stringent control over grain morphology, doping precision, and edge definition to ensure reliable CFET performance

Channel length modulation (CLM) and drain-induced barrier lowering (DIBL) are well-known short-channel effects that result in a finite output conductance (gds). In a simplified analysis, gds is predominantly governed by the drain voltage (VDS) and can be approximated by the slope of the IDS-VDS characteristics in the saturation regime. However, will the conceptual governance of gds be the same at the cryogenic temperatures? To answer this question, we thoroughly investigate the Cryogenic Nanosheet FET (NSFET) using well-calibrated TCAD models. The results reveal that incomplete ionization in the cryogenic temperature (CT) regime provides additional expansion of the depletion at the drain side. This significantly increases gds (i.e., the slope of the IDS-VDS characteristics) in CT compared to that at room temperature (RT). Therefore, in a Cryogenic FET, CLM becomes the function of temperature. Further, we extracted the CLM parameter (lambda), early voltage (VA), and intrinsic gain (gmax/gds) of the Nanosheet FET with varying temperatures and found that the gmax/gds is maximum at 4 K.

A Non-Equilibrium Green's Function (NEGF) simulation study on the impact of varying barriers and quantum well (QW) for a double barrier GaAs/Al0.3Ga0.7As Resonant Tunneling Diode (RTD) was carried out. This includes both variation of section thicknesses and the inclusion of interface roughness (IR) at different GaAs/Al0.3Ga0.7As interfaces. Narrower QWs and thinner symmetric barriers both resulted in a perturbation of Negative Differential Region (NDR) of the current-voltage (I-V) characteristic to greater bias. Asymmetric variation of the barriers controlled the perturbation of the resonant peak bias V,, with a thinner first barrier resulting in a perturbation to greater bias and a thinner second barrier resulting in the inverse. Both barrier thicknesses inversely impacted the current, with the first barrier having a greater impact. The impact of IR was studied using the average of 25 device I-V characteristics for a given configuration of IR, as well as the I-V characteristic and charge density of specific devices. It was found that IR along the QW reduced the effective QW width and IR along the barriers increased their effective thickness, which together explained the effects of IR along all Al0.3Ga0.7As interfaces.

In this study, we proposed a dielectric modulated (DM) Negative Capacitance Junctionless FinFET (JLNC-FinFET) to achieve precise label-free electrical detection of biomolecules, including streptavidin (K=2.1), biotin (K=2.63), APTES (K=3.57), and Keratin (K=8) etc., within the designated cavity region. The proposed approach employs variations in the threshold voltage for which the sensitivity (S-VTH) has a maximum value of 15.2% for Pyridine (K=12), serving as discerning metrics for detecting different neutral and charged biomolecules. The baseline Junctionless FinFET, which is fabricated and characterized, in our previous publication, is opted for this study. Realizing the ferroelectric layer over its baseline counterpart offers a notable I-ON/I-OFF improvement (similar to 10(4)) in JLNC FinFET. The reliability concerns of metal gate granularities (MGG) and line edge roughness (LER) have been considered to explore the impact on biomolecule detection, i.e., biosensor sensitivity. The results reveal that larger grain sizes exacerbate work function variability, especially with biomolecules resembled by high-K cavities. Moreover, the LER significantly impacts device parameters, which worsens the detection of biomolecules with high-K and LER amplitudes. Thus, the proposed study is worth exploring to acquire the design guidelines for reliability-aware biosensors.

The scaled geometry of vertically stacked Nanosheet FET (NSFET) is now in adaptable stage. However, the device geometry and inherent physical charge transport phenomenon is not yet well captured by the simulation studies. In some sense, the conventional drift-diffusion transport is not adequate and requires the inclusion of coupled Boltzmann transport equation (BTE). Further, at some instant, the ballistic model needs to be incorporated to capture the transport mechanism, where the mean-free path of the carrier is larger. To overcome this dilemma, in this work, we extensively analyzed the scattering effect on NSFET by employing different models viz (i) phonon scattering (PH); (ii) surface roughness (SR); (iii) coulomb scattering (CO). Further, we simulated the same baseline device considering the ballistic model using Sentaurs-QTX simulations. All the models are well-mapped with different geometrical dimensions and found that the cumulative effects of scattering events are rightly capturing the carrier transport, instead considering the ballistic model.

The vertical nanosheet ( channels) stacking and aligned in such a way that nFET is kept over pFET, or vice-versa, raises severe reliability concerns in Complementary FET (CFET). In this paper, using well- calibrated TCAD models, all the related reliability issues are being analyzed, such as: (i) the role of dielectric separation wall (D-SW) in electrical and thermal cross-talk from nFET to pFET and vice-versa; (ii) the impact of self-heating effect (SHE) on self- and the other side of the D-SW; (iii) impact of random dopant fluctuations (RDF) on threshold voltage (V-th); (iv) impact of line-edge roughness (LER) on I-ON and Vth; ( v) effect of metal grain granularities (MGG) and the ratio of grain size to gate area (RGG) on device merits, viz I-ON and V-th; and finally (vi) the device aging is predicated using the 'shift in V-th' by +/- 50mV. Thus, the proposed analysis benchmarks a reliable CFET design.

The Nano-Electronic Simulation Software (NESS) features an improved model of Interface Roughness (IR), accounting for correlation lengths in two perpendicular directions and allowing anisotropic roughness. IR in $$\text {GaAs/Al}_{0.3}\text {Ga}_{0.7}\text {As}$$ Resonant Tunnelling Diodes (RTDs) was investigated using both the previous and improved models, with 4 correlation lengths ( $$L_C$$ ) ranging from 2.5 nm to 10 nm. For each correlation length, 25 RTD device structures with IR were randomly generated. Device variation was quantified as the standard deviation of the resonant peak current ( $$I_r$$ ) and the corresponding bias voltage ( $$V_r$$ ), both extracted from the non-linear RTD current-voltage (IV) characteristics. The improved model resulted in greater variation, increasing standard deviation from 6.2 mV and 9 nA to 24.2 mV and 34.7 nA for $$L_C=2.5$$ nm. Standard deviation also roughly doubled as $$L_C$$ increased from 2.5nm to 10nm, increasing from 6.2 mV and 9 nA to 11.7 mV and 18.8 nA for the previous IR model, and from 24.2 mV and 34.7 nA to 38.8 mV and 80.9 nA for the improved IR model. A further study of anisotropic correlation lengths resulted in variation of standard deviations. This paper hence shows the importance of simulating IR with two correlation lengths for future accurate RTD research.

This work discusses the sensitivity response of a feedback field effect transistor-based ion sensor (ISFBFET). To precisely predict the sensing behavior, the Gouy-Chapman-Stern and site-binding methods are used as the principle models in a detailed TCAD study. For charge binding, the deposition of Si3N4 over SiO2 is used as a sensing element. The behavior of the ISFBFET is discussed against gate work function (Phi G) engineering and bulk pH. The performance of the sensor is analyzed using IDS-VDS, IDS-transit time, and snap-back characteristics. Sensitivity is evaluated in terms of constant current and constant voltage using snap-back characteristics and at Phi G = 4.4 eV, the highest sensitivity of 2.35 and 405 is obtained. The proposed work can help in designing FBFET based ion-sensors for sensing biomolecules or amino acids.

In this work we present a hierarchical computational approach to study the impact of source/drain access resistance in nanosheet transistors at the 3nm technology node and beyond. We employ the non-equilibrium Green's function (NEGF) approach to derive the current-voltage characteristics of the nanosheet transistors having extremely short source/drain extensions. Subsequently, we calibrate our quantum-corrected drift-diffusion simulator based on the density gradient formalism, which is then used to simulate structures with realistic lengths of source/drain regions. The device characteristics thus obtained reflect the impact of the access resistance. We analyse the impact of geometry scaling and doping levels in the Source/Drain extensions on the access resistance for different technology nodes.

In this work, an L-shaped Tunnel FET is demonstrated for the impact of the simulation models and temperature variations. When temperature increases above the room temperature (250K to 450K), it significantly affects the carrier mobility and carrier injection process. The SRH (Shockley-Read-Hall) and TAT (Trap-assisted Tunneling) show their significance in increasing the OFF -state current (ambipolar behavior) for the lower and negative values of gate voltage. When gate voltage rises, the BTBT model (Band-to-Band-Tunneling) starts to show its presence, and the impact of SRH and TAT models starts decreasing. Because of this, the OFF -state current starts diminishing as gate voltage increases. According to the applied electric field, the BTBT, SRH, and TAT current functionalities have particular confining regions. TAT and SRH aspects predominate drain current at weak electric fields, as they are very susceptible to temperature variations. Hence, the change in models and temperature affects the device efficacy, such as analog and high-frequency functionality, and it necessitates a detailed investigation.

In this work, we present simulation-based results of a vertical nanowire feedback field effect transistor as an ion-sensor (ISFBFET) for accurate pH detection. The sensing mechanism of the investigated ISFBFET draws upon the principles of the Gouy-Chapman-Stern layer and the site-binding model. Along with SiO2 only, the deposition of Al2O3 on SiO2 as a sensitive layer is investigated. Sensitivity parameters are obtained from snap-back characteristics and are evaluated against work function (ΦG) engineering. At optimized conditions, ISFBFET results in ION/IOFF = 1010 and an SS = 0.03mV/dec with Al2O3-SiO2 improving the ION/IOFF by a factor of 1.24 (ΦG = 4.7eV) and 5.53 (ΦG = 5.1eV) over an oxide only gate insulator. Up to ΦG≤4.3eV, the sensing response of SiO2 is marginally better; whereas for ΦG> 4.3eV, Al2O3-SiO2 has a better sensing response. Peak sensitivity for Al2O3 is observed at ΦG = 4.7eV, maximum constant current and voltage sensitivity observed is 3927 and 2.73 respectively. In the near future, due to low power functioning, steeper response, and high sensitivity, Al2O3-SiO2 based ISFBFET can replace conventional oxide-based ion sensors for pH detection in commercial applications.

In this study, we introduce a novel methodology to investigate and model the nonlinear perturbative behaviour in an Electrolyte-Gated Field-Effect Transistor (EGFET) based on biosensors that are enhanced with immobilised nanoparticles. Our approach systematically addresses the perturbations occurring due to redundant silanol sites by quantifying their impact on sensor output, thereby improving the reliability of the sensor readings. Additionally, we explore the role of neutral gold nanoparticles in augmenting the sensor ability to detect and differentiate amino acid fingerprints effectively. Moreover, our research delves into the effects of random partial hybridisation of carboxylic acids (amino acids) under varying conditions, which alter the reactive sites available for binding. By integrating these factors into our analysis, we provide a robust framework for calibrating and benchmarking experimental data.

In this work, a novel ultra-thin finger-like source region-based TFET (UTS-F-TFET) is used for the implementation of a temperature sensor or resistance temperature detector (RTD). Gate and source contact metal with specific work functions are deposited to provide proper functioning and detection of temperature deviation. Lower band gap material (Silicon-Germanium, SiGe) is used as source material to compensate for the limitation of lower ON-state current in the UTS-F-TFET. Horizontal as well as vertical tunneling with reduced drain-channel interface, conductive oxide (high-k) and exposed source and gate electrode are the key factors that are used to implement the RTD. ON-state current and gate biasing are used to calculate the effective change in resistance with fixed drain bias, so ON-state current has opted as the fundamental sensing parameters. The findings of the sensing parameters aid the possibility of using the proposed device for RTD applications.

Dielectric Modulated (DM) biosensors are being developed for label-free biosensing based on varying dielectric constants of the cavity region. In this paper, we proposed a GaAs(1-x)Sb(x )based cylindrical DM biosensor. The chosen device geometry provides enhanced gate control and increases cavity area compared to the planar devices. This bestows larger dwelling space to the biomolecules. In our work, we have investigated the sensing capability of the proposed biosensor for Biotin (k = 2.63), Bacteriophage (k = 6.3), and Gelatin (k = 12); whereas, the proposed study is applicable in all types of biomolecules which are characterized by the dielectric constants. The deviation in the effective oxide thickness (EOT) due to the variation in permittivity of the cavity area modulates the channel conductance and, in turn, ON current and threshold voltages (V-th). Thus, these parameters can be used for analyzing the sensitivity. For the gelatin biomolecule, the maximum computed sensitivity with regard to the OFF to ON current ratio and saturation current is 41.20% and 16.68%, respectively. Further, we also investigated the sensitivity metrics for the charge-carrying biomolecules using trap models employed in TCAD simulations.

The miniaturized and non-planar emerging FET-based sensors are in high demand owing to their intrinsic properties of steep switching characteristics, low power need, and higher sensitivity. However, they suffer from various reliability concerns, which require timely attention. In this paper, we employed Junctionless (JL) FinFET as a hydrogen (H2) gas sensor, which is already characterized for transient conditions in our previous publication. Using well-calibrated TCAD models, we thoroughly investigated (i) the impact of strained silicon on the sensitivity of the targeted JL-FinFET-based H2 sensor; (ii) the inclusion of the high mobility performance techniques to modulate the piezo resistance of strained JL (SJL) FinFET; (iii) the role of metal gate granularities (MGG) on SJL-FinFET sensing performance; and finally (iv) the early aging, i.e., end-of-lifetime (EOL) of the sensor using threshold voltage (a sensing metric) shift by ±50mV. Thus, it is worth analyzing the robustness of an H2 sensor for a strained FET-based sensor.

In this paper, we demonstrated the performance metrics of a Junctionless vertically stacked Forksheet FET for biosensing applications. Through the use of well-calibrated TCAD models, the N-and P-side of a Forksheet FET is designed with a cavity region where the presence/absence of the biomolecule is sensed using a permittivity modulation approach. The label-free electrical detection of different biomolecules, including streptavidin (K=2.1), biotin (K=2.63), APTES (K=3.57), and keratin (K=8) is obtained by analyzing the variations in threshold voltage, OFF current, and the ION/IOFF ratio as sensing performance metrics. The proposed Junctionless sensor exhibits superior electrical characteristics and sensitivity at lower metal gate work functions compared to other available Junctionless devices operating at higher metal gate work functions. Thus, the study underscores the potential of the proposed Junctionless Forksheet FET for efficient and accurate biosensing applications.

This study demonstrates a machine learning (ML)-assisted device circuit co-optimization technique. A basic CMOS inverter cell is employed to demonstrate this proof of concept. The voltage transfer and the switching characteristics are examined to observe the ON-and OFF-state behavior while applying two short square pulses of 140 ns and 100 ps, respectively. By applying the proposed ML-based optimization method using the actor and critic neural networks, the mixed-mode simulation i.e., technology computer-aided design (TCAD) and SPICE outputs toward the desired behavior of the circuit, and parameters, including area factor, doping concentration, capacitance value, and width and length of the device, are optimized. Compared with the manual design case, the co-optimized design surpasses several figures of merit (FoMs), such as propagation delay and overshoot, which pave the way for future research on more complex circuit design challenges.

The utilization of Technology Computer-Aided Design (TCAD) tools for the simulation of both conventional and new electronic devices has become a crucial aspect within the semiconductor industry, as well as in academic research. This paper presents a comprehensive overview of our research investigating the impact of random dopant fluctuations on “3nm node” silicon nanosheet field effect transistors (NSFETs) fabricated. Our study employs NESS (Nano-Electronic Software Simulator), the in-house device simulator developed by the Device Modelling Group at the University of Glasgow. The first principle study was performed to calculate the electrical parameter of the material, and later a fully 3-D real Non-equilibrium Green's function (NEGF) simulation was performed to capture the device characteristics.

This letter presents a simulation report on a modern, power-efficient, steep subthreshold slope (SS) electron-hole bilayer tunnel field-effect transistor (EHB-TFET) as an ion sensor. The effect of variation in channel thickness and work function engineering (of both top and bottom gates) on the transversal electron tunneling rate and I-DS-V-GS characteristics is also discussed. At optimized values, the EHB-TFET results in low I-off (=10(-18) A/mu m) and SS of 13 mV/dec. The Gouy-Chapman-Stern and the site-binding model form the basis for assessing EHB-TFET's sensing mechanism. The top gate is assumed to be the controlling gate, and as the bottom gate insulator, along with SiO2, the sensitivity response of ZrO2 and Ta2O5 is also examined. The sensitivity behavior is evaluated in terms of threshold voltage, and it was observed that sensitivity is directly proportional to the surface potential. The highest sensitivity obtained is for ZrO2 followed by SiO2 and Ta2O5. Due to the evident advantages of EHB-TFET in the near future, it can be used as a low-power, quick-responsive, reliable ion sensor and can be used to replace conventional industrial sensors.

This paper reveals the role of Flicker (1/f) Noise and process variations (i.e., random dopant fluctuation, RDF) on the sensitivity of the Junctionless FinFET-based hydrogen gas (H2) sensor with a ferroelectric (FE) gate stack, which offers the privilege of Negative Capacitance (NC) effect. In general, the FE-stack has two possible configurations, i.e., MFMIS and MFIS. Therefore, the sustainability and selectivity of both configurations under the influence of Noise on the sensor's sensitivity have been thoroughly investigated using well-calibrated TCAD models. With varying H2 concentrations (in ppm) and FE thicknesses in both configurations, the acquired electrical characteristics, sensing metrics, and noise spectral density (S IDS ) reveal that the MFMIS is an appropriate choice for realizing a FET -based sensor.