Gallium Nitride High Electron Mobility Transistors (GaN-on-Si HEMTs) on silicon substrates exhibit significant potential in the field of radio-frequency devices due to its high frequency, high-speed, and low-cost advantages. Nevertheless, the thermal stress caused by the mismatch in lattice parameters and the coefficient of thermal expansion between the Si substrate and GaN epitaxial layer constrains the device performance. Due to the different temperature distributions in different scenarios, there is an urgent need for a model that can balance the contradiction between epitaxial growth and device operation for the joint design of GaN-on-Si HEMTs epitaxial structures. This work presents thermal stress formulas specifically designed for GaN epitaxial growth and device operation, based on Townsend’s theory of multilayer film curvature and stress. Furthermore, our work innovatively proposes a multi-scenario coupled GaN-on-Si HEMTs epitaxial structure design model based on multi-objective optimization. The model represents a combined objective function incorporating thermal stress experienced during device operation and epitaxial growth, as well as the total thermal resistance and bulk thermal conductivity of the epitaxial layer. Through simulation validation, the model has been proved to effectively calculate the optimized epitaxial structure parameters across multiple scenarios to meet diverse requirements. And it can effectively reduce the operating temperature. This model can accelerate the process of GaN epitaxial design and provide an effective method for enhancing the performance of GaN-on-Si HEMTs.

In existing consensus models with optimization approaches, the modified preferences are often virtual values unrelated to the original rating scale. Furthermore, few optimization approaches have been developed to specifically deal with the consensus reaching process in uncertain multiple-criteria group decision making (MCGDM) problems. To investigate these issues, this paper is primarily interested in MCGDM optimization consensus models, in which the uncertain information can be represented using interval numbers. Integer optimization consensus models are established based on both the Manhattan distance and the Euclidean distance, and an antithetic-method-based particle swarm optimization algorithm is used to solve the nonlinear integer programming problems. Compared to existing approaches, the main characteristic of the proposed consensus models is that the generated preferences all belong to the original scales. A numerical example is given to validate the proposed models, and further simulation analyses shed light on the behavior of the developed models.

This paper focuses on investigating the cross-border financial contagion based on a fuzzy dynamical system scenario simulation from a perspective of analyzing the volatility of international capital flows for a panel of 50 countries in emerging markets and advanced economies from 1980 to 2011. Increasing evidence has shown that financial globalization has developed into a complex nonlinear dynamical system made up of economic subsystems with extensive financial connections and linkages. The contagion effects of the spread of bonanzas in the 50 countries are identified and analyzed. The Hodrick-Prescott filter is employed to address the long-term net capital inflow trend. The comovement of financial contagion between the source country of financial turbulence and the volatility-affected country is described as a fuzzy dynamical system in which the driving and response systems are coupled. A fuzzy dynamical system scenario simulation model under a liberal economy is established by employing nonlinear differential equations to describe the contagion mechanism and the international capital flow volatility effects. The model is then extended to a dynamical system model with macroeconomic control. The coupling strength uncertainty is addressed by employing an interval type-2 fuzzy theory method. The properties of the volatility equilibrium point for the two models are discussed, and the volatility contagion principles based on locally asymptotic stability analysis are derived to explain the different volatility transmission patterns. Policy suggestions are given in three situations for providing managerial insights for policymakers and the explorations of response strategies are also presented. The global financial crisis in 2008 is used as an experimental study to demonstrate the validity and effectiveness of the simulation and modeling method.