In this paper, a vertically stacked nanosheet gate-all-around field-effect transistor (GAA-NSFET) as a label-free biosensor has been proposed and investigated. The influences of different biomolecules on the biosensor's electrical properties are analyzed, and the proposed biosensor exhibits good sensitivity for both neutral and charged biomolecules. Furthermore, a deep sensitivity analysis is performed to evaluate the sensing ability of biosensors with different channel structures. The sensitivity variation of biosensors is analyzed in terms of subthreshold swing ( SS), threshold voltage ( V _th ), and current switching ratio ( I _on / I _off ). The results show that GAA-NSFET-based biosensor obtains the best sensitivity compared with other biosensors (including nanowire FET-based biosensor and vertically stacked nanowire FET-based biosensor) due to its larger channel width and multi-channels. Also, the influences of filling position on sensitivity are analyzed in various cases for the proposed biosensor. Its sensitivity depends on the filling amounts of biomolecules instead of the filling position. Finally, a status map is presented, which plots the sensitivity of some important works in biosensing application along with the sensitivity of the proposed biosensor, and GAA-NSFET-based biosensor is more sensitive compared with those works in terms of I _on /I _off sensitivity.

In this article, an inverted T-shaped tunnel field-effect transistor (TFET) with a dual metal gate (DMG) as a label-free biosensor has been proposed and analyzed using 3-D technology computer aided design (TCAD) simulations. The article compares the traditional inverted T-type TFET and the inverted T-type TFET with the DMG structure when used as a sensor. We focused on analyzing the electrical characteristics and sensitivity of the DMG structure and the single metal gate (SMG) structure as biosensors. The results show that the inverted T-shaped TFET biosensor with the DMG structure has better sensitivity and electrical characteristics. Compared with the SMG (work function = 4.3 eV), the DMG has a better ON-state current; compared with the SMG (work function = 3.8 eV), the DMG has a better subthreshold swing and sensitivity. In addition, the article also analyzed the influence of factors such as filling degree and temperature on the biosensor. The results show that the higher the filling level, the greater the ON-state current of the sensor. The higher the temperature, the greater the OFF-state current and the lower the sensitivity of the current switching ratio. Finally, a status map is presented, which maps the sensitivities of some important jobs in biosensor applications and the sensitivities of the proposed biosensors. The $I_{\text{on}} / I_{\text{off}}$ sensitivity of the inverted T-shaped TFET biosensor is superior to those jobs.

With the rapid development of semiconductor technology, traditional equation-based modeling faces challenges in accuracy and development time. To overcome these limitations, neural network (NN)-based modeling methods have been proposed. However, the NN-based compact model encounters two major issues. Firstly, it exhibits unphysical behaviors such as un-smoothness and non-monotonicity, which hinder its practical use. Secondly, finding an appropriate NN structure with high accuracy requires expertise and is time-consuming. In this paper, we propose an Automatic Physical-Informed Neural Network (AutoPINN) generation framework to solve these challenges. The framework consists of two parts: the Physics-Informed Neural Network (PINN) and the two-step Automatic Neural Network (AutoNN). The PINN is introduced to resolve unphysical issues by incorporating physical information. The AutoNN assists the PINN in automatically determining an optimal structure without human involvement. We evaluate the proposed AutoPINN framework on the gate-all-around transistor device. The results demonstrate that AutoPINN achieves an error of less than 0.05%. The generalization of our NN is promising, as validated by the test error and the loss landscape. The results demonstrate smoothness in high-order derivatives, and the monotonicity can be well-preserved. We believe that this work has the potential to accelerate the development and simulation process of emerging devices.