Optimization of Multi-Fins FinFET Implemented on SOI Wafer Based on SiGe and Gaussian Process Regression

Despite advancements in mitigating the short channel effect using high-k materials, multi-gate structures, and silicon-germanium (SiGe) alloys in three-dimensional FinFETs, performance trade-offs remain. This study introduces a novel machine learning framework utilizing a Gaussian process regression model (GPRM) and surrogate optimization (SO) to optimize design parameters of n-type and p-type SiGeFinFETs. With this approach targeting switching ratio (SR), the optimal mole fractions of the n-type FinFET are Si0.7Ge0.3 for source extension (S-ext), Si0.2Ge0.8 for channel (L-g), and Si for drain extension (D-ext),achieving an SR of 6.9x10(9). For p-type FinFET, the optimal configuration is Si0.9Ge0.1 for Sext, and Si for L-g and D-ext with an SR of 5.81x10(7). The optimization of n-type and p-type multi-fin FinFETs(NmFinFET and PmFinFET) was also investigated, considering varied input parameters such as L-g, fin height(F-h), fin width (F-w), S-ext, D-ext, and the number of fins (numfin). The optimized devices for NmFinFET and PmFinFET, prioritizing speed, have the same dimensions: L-g = 10 nm, F-h = 42 nm,F-w = 10 nm, S-ext = 3 nm, D-ext = 4 nm, and numfin = 5. An inverter constructed using these optimized parameters showed a simulated propagation delay of 2 ps. This machine learning-driven approach demonstrates remarkable effectiveness in optimizing FinFET designs. The framework's ability to simultaneously optimize multiple objectives showcases its potential for advancing semiconductor device engineering.