In this work, a high-sensitivity terahertz (THz) biosensor is achieved by using a graphene/Bragg reflector hybrid structure. This high-sensitivity THz biosensor is developed from the sharp Fano resonance transmission peak created by coupling the graphene Tamm plasmons (GTPs) mode to a defect mode. It is found that the proposed THz biosensor is highly sensitive to the Fermi energy of graphene, as well as the thickness and refractive index of the sensing medium. Through specific parameter settings, the composite structure can achieve both a liquid biosensor and a gas biosensor. For the liquid biosensor, the maximum sensitivity of $\text{>} 1000 °/\mathrm{RIU}$ is obtained by selecting appropriate parameters. We believe the proposed layered hybrid structure has the potential to fabricate graphene-based high-sensitivity biosensors.

In this work, we present a theoretical model of a near-infrared sensitive refractive index biosensor based on the truncate 1D photonic crystal (1D PC) structure with Dirac semimetal. This highly sensitive near-infrared biosensor originates from the sharp reflectance peak caused by the excitation of Bloch surface wave (BSW) at the interface between the Dirac semimetal and 1D PC. The sensitivity of the biosensor model is sensitive to the Fermi energy of Dirac semimetal, the thickness of the truncate layer and the refractive index of the sensing medium. By optimizing the structural parameters, the maximum refractive index sensitivity of the biosensor model can surpass 17.4 × 10³/RIU, which achieves a certain competitiveness compared to conventional surface plasmon resonance (SPR) or BSW sensors. Considering that bulk materials are easier to handle than two-dimensional materials in manufacturing facilities, we judge that 3D Dirac semimetal and its related devices will provide a strong competitor and alternative to graphene-based devices.

Optimizing static random-access memory (SRAM) cells requires considering parasitic effects, as their impact on circuits in advanced nodes becomes increasingly complex. In this paper, Convolutional Neural Network-Informed Non-dominated Sorting Genetic Algorithms-II (CNN-Informed NSGA-II) was proposed to optimize 7 nm FinFET 6T-SRAM cells taking into account parasitic resistance. CNN-Informed NSGA-II uses a trained CNN model integrated into the conventional NSGA-II, thereby reducing its computational complexity. This approach provides a generally applicable solution that significantly improves the efficiency of circuits while balancing competitive performance metrics. Compared to the ideal (parasitic-free) 6T-SRAM cell design, the optimized 6T-SRAM cell design (considering parasitic effects) achieves a reduction of 81.60% in Write Dynamic Power and 64.65% in Write Time; HSNM and RSNM are improved by 11.92% and 6.42%, respectively. The optimized 7 nm FinFET 6T-SRAM cell structure in this paper outperforms the parasitic-free structure in terms of the performance parameters above, even when taking into account parasitic effects.