In this study, conventional CMOS and complementary field-effect transistor (CFET) inverters based on a vertically stacked-nanosheet (NS) structure were fabricated. The NS below 8-nm channel layer thickness (TSi) was obtained by dry etching and wet etching processes. The channel thickness is controlled by dry etching, and the channel width was shrunk down by wet etching. Compared to single nanowire field-effect transistors (NSFETs), stacked NSFETs exhibit higher on-current performance. For the CMOS inverter, the voltage transfer characteristics (VTCs) could be matched much better by adjusting the channel widths and layers for N-channel MOSFET (NFET) and P-channel MOSFET (PFET), respectively. For the CFET inverter, layout areas could be reduced and requires less number of lithographic and ion implantation steps contrary to the CMOS inverter. However, we observe that the VTCs of the CFET inverters still show asymmetric behavior due to the difficulties of adjustment in NS layers and systematic behavior of threshold voltages for NFETs/PFETs. This work experimentally demonstrates the CMOS and CFET inverters on the vertically stacked NS structure, which is promising for system-on-panel (SoP) and 3-D-ICs applications.

In this article, heterogeneous complementary field-effect-transistor (CFET) constructed by vertically stacking amorphous indium gallium zinc oxide (a-IGZO) n-channel on poly-Si p-channel with their own dielectric layer and work function metal gate inverters were demonstrated. Meanwhile, high-frequency IGZO radio frequency (RF) devices with poly-Si as guard ring material simultaneously were fabricated in the same process. High ${f}_{\text {T}}$ and ${f}_{\text {max}}$ IGZO Radio Frequency Integrated Circuits (RFICs) with the excellent on–off ratio need to be promoted by introducing fluorine-based gas. For the IGZO device in CFET, its threshold voltage can be tuned by the adjusted gate for ideal inverter operation at different supply voltage ( ${V}_{\text {DD}}$ ). Moreover, the swing of the IGZO transistor and the gain extracted from voltage transfer characteristic (VTC) curves can also be improved when the controlled gate and adjusted gate are connected as an input terminal, but the ${V}_{\text {TH}}$ tunability for the inverter is satisfied in the meantime. We also simulated 6T-SRAM circuit by SPICE model to further investigate the potential of an adjusted gate for optimizing the noise margin during SRAM operation.

According to quantum transport simulations, source-to-drain tunneling (SDT) has been recognized as the main cause leading to subthreshold swing (SS) saturation and degradation of short-channel MOSFETs at cryogenic temperatures. Generally, at a given low temperature, the steeper constant SS of thermionic currents may be overwhelmed by less-steep SDT currents at lower gate bias, degrading the average SS. Our simulations show that the SS of SDT currents is insensitive to temperatures for a MOSFET with a given channel length, accounting for SS saturation as lowering temperature. This work reveals the key points differentiating the possible reasons (interface traps, band tail and SDT) of SS saturation at cryogenic temperatures. We also study the impacts of carrier effective masses, gate-SD underlapping and a tunneling barrier at the source junction on SS saturation. SDT may pose a potential challenge and limit for scaling cryogenic CMOS of a quantum processor.

A synergistic approach for optimizing devices, circuits, and neural network architectures was used to abate junction-temperature-change-induced performance degradation of an Fe-FinFET-based artificial neural network. We demonstrated that the digital nature of the binarized neural network, with the “0” state programmed deep in the subthreshold and the “1” state in strong inversion, is crucial for robust deep neural network inference. The performance of a purely software-based binary neural network (BNN), with 96.1% accuracy for Modified National Institute of Standards and Technology (MNIST) handwritten digit recognition, was used as a baseline. The Fe-FinFET-based BNN (including device-to-device variation at 300 K) achieved 95.7% inference accuracy on the MNIST dataset. Although substantial inference accuracy degradation with temperature change was observed in a nonbinary neural network, the BNN with optimized Fe-FinFETs as synaptic devices had excellent resistance to temperature change effects, and maintained a minimum inference accuracy of 95.2% within a temperature range of −40 to 125 °C after gate stack and bias optimization. However, reprogramming to adjust device conductance was necessary for temperatures higher than 125 °C.

In this paper, a compact drain current formulation that is simple and adequately computationally efficient for the simulation of neural network online training was developed for the ferroelectric memory transistor. Tri-gate ferroelectric field-effect transistors (FETs) with Hf _0.5 Zr _0.5 O ₂ gate insulators were fabricated with a gate-first high-k metal gate CMOS process. Ferroelectric switching was confirmed with double sweep and pulse programming and erasure measurements. Novel characterization scheme for drain current was proposed with minimal alteration of ferroelectric state in subthreshold for accurate threshold voltage measurements. The resultant threshold voltage exhibited highly linear and symmetric across multilevel states. The proposed compact formulation accurately captured the FET gate-bias dependence by considering the effects of series resistance, Coulomb scattering, and vertical field dependent mobility degradation.

In this paper, one proposed an effective method to enhance current drivability of junctionless FETs (JL-FETs) by utilizing uniaxial tensile strain effects. The strained layers were deposited on JL-FETs on silicon-on-insulator (SOI) and bulk Si wafers, respectively. Strained JL SOI FETs show an extremely low subthreshold swing (S.S.) of 65 mV/decade with I _ON /I _OFF > 10 ⁹ ; strained JL bulk FinFETs show an S.S. of 75 mV/decade with I _ON /I _OFF > 10 ⁷ . For strained JL bulk FinFETs, a triangular fin shape could suppress leakage current effectively. Regardless of substrates, JL FETs showed excellent performance owing to uniaxial tensile strain technology. Analysis of leakage current in strained JL FETs included effects on Gate-induced drain leakage trap-assisted tunneling effects were discussed by I _D -V _G curves under various temperatures and activation energy. Compared with JL SOI gate-all-around structures, JL bulk FinFET possesses higher I _D and offer the promise of higher integration flexibility for Si CMOS compatible process for the future applications.