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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 SiGe FinFETs. 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 $6.9\times 10 ^{9}$ . For p-type FinFET, the optimal configuration is Si0.9Ge0.1 for $S_{ext}$ , and Si for $L_{g}$ and $D_{ext}$ with an SR of $5.81\times 10 ^{7}$ . 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. |
