In this article, we present a physics-based gate current model for p-GaN/AlGaN/GaN high electron mobility transistors (HEMTs), valid across a wide bias, and broad temperature range. Unlike the existing gate current models valid upto nominal temperatures only, this work for the first time provides a modeling framework for gate leakage of p-GaN HEMTs valid down to cryogenic temperatures of 10 K. The model is based on various leakage mechanisms, with each mechanism determining the conduction across different gate bias and temperature ranges. By leveraging the device electrostatics and incorporating the characteristic effects associated with cryogenic temperatures such as bandgap widening and incomplete ionization, a unified gate current model for p-GaN HEMTs is established. The proposed model is benchmarked against both in-house experimental data and measurement results available in the literature to ascertain its validity across a wide range of bias and temperature. This work aims to serve as a fast and efficient tool for the reliable circuit design using p-GaN gate HEMTs for advanced cryogenic applications.
Mode-division multiplexing switches with larger N × N scales remain largely unexplored due to high optical losses, limiting their scalability. To address this, we present a highly scalable dual-mode ($\text{TE}_{0}/\text{TE}_{1}$) $N\times N$ ($N=2,4,8$) photonic switch. The design employs a non-blocking rectangular Clements topology, enabling arbitrary unitary matrix decomposition for both modes and increasing routing degrees of freedom. The proposed structure utilizes an optimized ultra-low-loss dual-mode directional coupler at $\text{1550}~\text{nm}$. The $8\times 8$ switch, it achieves low insertion losses of $\text{0.089}~\text{dB}$ ($\text{TE}_{0}$) and $\text{0.120}~\text{dB}$ ($\text{TE}_{1}$), with crosstalk of $-43.76~\text{dB}$ and $-41.29~\text{dB}$, respectively. Occupying a footprint of $\sim 8604~\mu \mathrm{m}\times 150~\mu \mathrm{m}$, the switch exhibits thermal response times of $16.76~\mu \mathrm{s}$ (rise) and $22.60~\mu \mathrm{s}$ (fall). Additionally, an added tuning layer enables post-fabrication phase error correction to enhance scalability and robustness. Successful $\text{25}~\text{Gbps}$ OOK data transmission analysis shows its strong potential for scalable, programmable multimode optical interconnects for telecommunication and computing applications.
In this work, a silicon-on-insulator based heterojunction tunnel field-effect transistor (SOI-HJ-TFET) biosensor with nanocavity is proposed first time for the detection of key lung cancer biomarkers, namely carcinoembryonic antigen (CEA), cytokeratin-19 fragment 21-1 (CYFRA 21-1), and neuron-specific enolase (NSE). The proposed device utilizes a low-bandgap magnesium silicide ($Mg_{2}Si$) source to enhance band-to-band tunneling, improving sensitivity. The nanocavity is functionalized with an APTES-glutaraldehyde matrix for the immobilization of biomarker molecules. The detection methodology employs a charge deduction approach, where biomarker concentrations are converted into an equivalent interface charge, modulating the device's electrical characteristics. Using calibrated SILVACO ATLAS TCAD simulations, the device's performance is evaluated for both non-tumorigenic (NTG) and tumorigenic (TG) cell concentrations. The results demonstrate that the presence of cancerous cells leads to a significant reduction in drain current, electron concentration, and band-to-band tunneling rate due to a higher negative equivalent interface charge. The biosensor exhibits high inverse sensitivity, with values up to 12×$10^{3}$ for TG CYFRA 21-1 cells. Key performance metrics, including changes in subthreshold swing ($\Delta SS$ up to 203 mV/decade) and threshold voltage ($\Delta V_{T}$ up to 637 mV), provide clear differentiation between healthy and cancerous CEA and CYFRA 21-1 cells, respectively. Additionally, sensing time analysis shows rapid response ranging from nanoseconds to microseconds, making the device suitable for real-time biosensing. The proposed SOI-HJ-TFET biosensor exhibits high inverse sensitivity, fast response, and strong inverse selectivity, particularly for CYFRA 21-1, indicating its potential for efficient early-stage lung cancer detection in point-of-care diagnostic platforms.
