Modeling and Simulation of Low-Frequency Noise in Nano Devices: Stochastically Correct and Carefully Crafted Numerical Techniques PROJECT TITLE :Modeling and Simulation of Low-Frequency Noise in Nano Devices: Stochastically Correct and Carefully Crafted Numerical TechniquesABSTRACT:Defects or traps in semiconductors and nano devices that randomly capture and emit charge carriers end in low-frequency noise, like burst and 1/f noise, which are vital concerns in the design of both analog and digital circuits. The capture and emission rates of these traps are functions of the time-varying voltages across the device, resulting in nonstationary noise characteristics. Modeling of low-frequency, nonstationary noise in circuit simulators is a long-standing open downside. It's been realized that the low-frequency noise models in circuit simulators were the culprits that produced erroneous noise performance results for circuits below strongly time-varying bias conditions. In this paper, we tend to gift 2 absolutely nonstationary models for traps, a fine-grained Markov chain model and a rough-grained Langevin model primarily based on similar models for ion channels in neurons. The nonstationary trap models we gift subsume and unify all of the work that has been done recently within the device modeling and circuit design literature on modeling nonstationary lure noise. We have a tendency to offer a detailed explication of these models with regard to their stochastic properties and develop fastidiously crafted circuit simulation techniques that are stochastically correct. We have a tendency to have implemented the proposed techniques in a MATLAB-primarily based circuit simulator, by expanding the industry standard compact MOSFET model PSP to incorporate a nonstationary description of oxide traps. We have a tendency to present results obtained by this extended model and also the proposed simulation techniques for the low-frequency noise characterization of a common supply amplifier and therefore the phase jitter of a hoop oscillator. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Data Remapping for Static NUCA in Degradable Chip Multiprocessors Intelligent Patient Management and Resource Planning for Complex, Heterogeneous, and Stochastic Healthcare Systems