Accurate extraction of the trap position in the oxide in deep-submicron MOSFET by RTN measurement has been investigated both theoretically and experimentally. The conventional equation based on the ratio of emission time and capture time ignores two effects, that is, the poly gate depletion effect and surface potential variation in strong inversion regime. In this paper, by including both of the two effects, we have derived a new equation which gives us more accurate information of the trap depth from the interface and the trap energy. With experimental result, we compare the trap depth obtained from the new equation and that of the conventional method.

In this letter, the absolute exponential stability result of neural networks with asymmetric connection matrices is obtained, which generalizes the existing one about absolute stability of neural networks, by a new proof approach. It is demonstrated that the network time constant is inversely proportional to the global exponential convergence rate of the network trajectories to the unique equilibrium. A numerical simulation example is also given to illustrate the obtained analysis results.

In this letter, we obtain the absolute exponential stability result of neural networks with globally Lipschitz continuous, increasing and bounded activation functions under a sufficient condition which can unify some relevant sufficient ones for absolute stability in the literature. The obtained absolute exponential stability result generalizes the existing ones about absolute stability of neural networks. Moreover, it is demonstrated, by a mathematically rigorous proof, that the network time constant is inversely proportional to the global exponential convergence rate of the network trajectories to the unique equilibrium. A numerical simulation example is also presented to illustrate the analysis results.