Process variability is a major challenge for the design of nano scale MOSFETs due to fundamental physical limits as well as process control limitations. As the size of the devices is scales down to improve performance, the circuit becomes more sensitive to the process variations. Thus, it is necessary to have a device model that can predict the variations of device characteristics. Statistical modeling method is a potential solution for this problem. The novelty of the work is that we connect BSIM parameters directly to the underlying process parameters. This is very useful for fabs to optimize and control the specific processes to achieve certain circuit metric. This methodology and framework is extendable to any future technologies, because we used a device independent, but process depended frame work In the first part of this thesis, presents the design of nominal MOS devices with 28 nm physical gate length. The device is optimized to meet the specification of low standby power technology specification of International Technology Roadmap for Semiconductors ITRS(2012). Design of experiments are conducted and the following parameters gate length, oxide thickness, halo concentration, anneal temperature and title angle of halo doping are identified as the critical process parameters. The device performance factors saturation current, sub threshold current, output impendence and transconductance are examined under process variabilty. In the subsequent sections of the thesis, BSIM parameter extraction of MOS devices using the software ICCAP is presented. The variability of the spice parameters due to process variation is extracted. Using the extracted data a new BSIM interpolated model for a variability aware circuit design is proposed assume a single process parameter is varying. The model validation is done and error in ICCAP extraction method for process variability is less than 10% for all process variation condition in 3σ range. In the next section, proposes LUT model and interpolated method for a variability aware circuit design for single parameter variation. The error in LUT method for process variability reports less than 3% for all process variation condition in 3σ range. The error in perdition of drain current and intrinsic gain for LUT model files are very close to the result of device simulation. The focus of the work was to established effective method to interlink process and SPICE parameters under variability. This required generating a large number of BSIM parameter ducks. Since there could be some inaccuracy in large set of BSIM parameters, we used LUT as a golden standard. We used LUT modeling as a benchmark for validation of our BSIM3 model In the final section of thesis, impact of multi parameter variation of the processes in device performance is modelled using RSM method; the model is verified using ANOVA method. Models are found to be sufficient and stable. The reported error is less than 1% in all cases. Monte Carlo simulation confirms stability and repeatability of the model. The model for random variabilty of process parameters are formulated using BSIM and compared with the LUT model. The model was tested using a benchmark circuit. The maximum error in Monte Carlo simulation is found to be less than 3% for output current and less than 8% for output impedance.