A Computationally Efficient Model for FDSOI MOSFETs and Its Application for Delay Variability Analysis - Zhiyi Mao; Yuping Wu; Lan Chen; Xuelian Zhang (Applied Sciences, 2022)
This paper proposes a compact, physics-based current model for fully depleted silicon-on-insulator (FDSOI) MOSFETs and applies it to delay variability analysis. An analytical method is applied to avoid the numerical iterations required in the evaluation of surface potential, which directly improves the computational efficiency. The accuracy of the explicit surface potential approximation is 190.3 nV, which allows for fast convergence. Surface potential and current calculations achieve 1.8× and 1.4× acceleration compared with BSIM-IMG, respectively. To establish the relationship between delay and underlying process parameters, we introduce the effective current and propose a process variation-aware delay prediction model. Higher-order derivatives are calculated to compensate the nonlinearity of delay variations with respect to process parameters. Experiments show a significant improvement in the prediction accuracy with higher-order derivatives, which are proved to be able to handle nonlinearity under process variations. The front gate work function contributes the most to the nonlinearity of the delay variation and the accuracy of the third-order prediction is 4.07%. Under the variation in the channel length and width, front and back gate oxide thickness and body thickness, delay variations have similar characteristics and the second-order prediction is found to be sufficient to model the nonlinearity with a maximum relative error of 1.22%. The delay prediction model only requires a single-point HSPICE DC or transient simulation and is universal for different voltages and different cells. Compared with the Monte Carlo (MC) simulation, the accuracy of the first-order prediction in the above-threshold region (0.8 V) is 0.94%. In the sub-threshold region (0.3 V), a prediction accuracy of 2.01% can be obtained while achieving a 21× reduction in computational time.