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We report the design-technology co-optimization (DTCO) scheme to develop a 28-nm cryogenic CMOS (Cryo-CMOS) technology for high-performance computing (HPC). The precise adjustment of halo implants manages to compensate the threshold voltage (VTH) shift at low temperatures. The optimized NMOS and PMOS transistors, featured by VTH<0.2V, sub-threshold swing (SS)<30 mV/dec, and on-state current (Ion)>1.2mA/um at 77K, warrant a reliable sub-0.6V operation. Moreover, the enhanced driving strength of Cryo-CMOS inherited from a higher transconductance leads to marked improvements in elevating the ring oscillator frequency by 20%, while reducing the power consumption of the compute-intensive cryogenic IC system by 37% at 77K.

Graphene-based 2D heterostructures exhibit excellent mechanical and electrical properties, which are expected to exhibit better performances than graphene for nanoelectromechanical pressure sensors. Here, we built the pressure sensor models based on suspended heterostructures of graphene/h-BN, graphene/MoS2, and graphene/MoSe2 by using COMSOL Multiphysics finite element software. We found that suspended circular 2D membranes show the best sensitivity to pressures compared to rectangular and square ones. We simulated the deflections, strains, resonant frequencies, and Young's moduli of suspended graphene-based heterostructures under the conditions of different applied pressures and geometrical sizes, built-in tensions, and the number of atomic layers of 2D membranes. The Young's moduli of 2D heterostructures of graphene, graphene/h-BN, graphene/MoS2, and graphene/MoSe2 were estimated to be 1.001TPa, 921.08 GPa, 551.11 GPa, and 475.68 GPa, respectively. We also discuss the effect of highly asymmetric cavities on device performance. These results would contribute to the understanding of the mechanical properties of graphene-based heterostructures and would be helpful for the design and manufacture of high-performance NEMS pressure sensors.

Graphene has the great potential to be used for humidity sensing due to ultrahigh surface area and conductivity. However, the impact of different atomic layers of graphene on SiO2/Si substrate on the humidity sensing have not been studied yet. In this paper, we fabricated three types of humidity sensors on SiO2/Si substrate based on one to three atomic layers of graphene, in which the sensing areas of graphene are 75 {\mu}m * 72 {\mu}m and 45 {\mu}m * 72 {\mu}m, respectively. We studied the impact of both the number of atomic layers of graphene and the sensing areas of graphene on the responsivity and response/recovery time of the prepared graphene-based humidity sensors. We found the relative resistance change of the prepared devices decreased with the increase of number of atomic layers of graphene under the same change of relative humidity. Further, devices based on tri-layer graphene showed the fastest response/recovery time while devices based on double-layer graphene showed the slowest response/recovery time. Finally, we chose the devices based on double-layer graphene that have relatively good responsivity and stability for application in respiration monitoring and contact-free finger monitoring.

Graphene ribbons with a suspended proof mass for nanomechanical systems have been rarely studied. Here, we report three types of nanomechanical devices consisting of graphene ribbons (two ribbons, four ribbons-cross and four ribbons-parallel) with suspended Si proof masses and studied their mechanical properties. The resonance frequencies and built-in stresses of three types of devices ranged from tens of kHz to hundreds of kHz, and from 82.61 MPa to 545.73 MPa, respectively, both of which decrease with the increase of the size of proof mass. The devices with four graphene ribbons featured higher resonance frequencies and spring constants, but lower built-in stresses than two ribbon devices under otherwise identical conditions. The Young's modulus and fracture strain of double-layer graphene were measured to be 0.34 TPa and 1.13% respectively, by using the experimental data and finite element analysis (FEA) simulations. Our studies would lay the foundation for understanding of mechanical properties of graphene ribbons with a suspended proof mass and their potential applications in nanoelectromechanical systems.

Graphene is being increasingly used as an interesting transducer membrane in micro- and nanoelectromechanical systems (MEMS and NEMS, respectively) due to its atomical thickness, extremely high carrier mobility, high mechanical strength and piezoresistive electromechanical transductions. NEMS devices based on graphene feature increased sensitivity, reduced size, and new functionalities. In this review, we discuss the merits of graphene as a functional material for MEMS and NEMS, the related properties of graphene, the transduction mechanisms of graphene MEMS and NEMS, typical transfer methods for integrating graphene with MEMS substrates, methods for fabricating suspended graphene, and graphene patterning and electrical contact. Consequently, we provide an overview of devices based on suspended and nonsuspended graphene structures. Finally, we discuss the potential and challenges of applications of graphene in MEMS and NEMS. Owing to its unique features, graphene is a promising material for emerging MEMS, NEMS and sensor applications.

Optimization problems with access to only zeroth-order information of the objective function on Riemannian manifolds arise in various applications, spanning from statistical learning to robot learning. While various zeroth-order algorithms have been proposed in Euclidean space, they are not inherently designed to handle the challenging constraints imposed by Riemannian manifolds. The proper adaptation of zeroth-order techniques to Riemannian manifolds remained unknown until the pioneering work of \cite{li2023stochastic}. However, zeroth-order algorithms are widely observed to converge slowly and be unstable in practice. To alleviate these issues, we propose a Riemannian accelerated zeroth-order algorithm with improved robustness. Regarding efficiency, our accelerated algorithm has the function query complexity of $\mathcal{O}(\epsilon^{-7/4}d)$ for finding an $\epsilon$-approximate first-order stationary point. By introducing a small perturbation, it exhibits a function query complexity of $\tilde{\mathcal{O}}(\epsilon^{-7/4}d)$ for seeking a second-order stationary point with a high probability, matching state-of-the-art result in Euclidean space. Moreover, we further establish the almost sure convergence in the asymptotic sense through the Stable Manifold Theorem. Regarding robustness, our algorithm requires larger smoothing parameters in the order of $\tilde{\mathcal{O}}(\epsilon^{7/8}d^{-1/2})$, improving the existing result by a factor of $\tilde{\mathcal{O}}(\epsilon^{3/4})$., Comment: Accepted by ICML 2024