FuncAnoDe: A Function Level Anomaly Detection in Device Simulation

TaeIL Oh, Hong Chul Nam, Chanwoo Park, Hyunbo Choi

Abstract: In semiconductor device simulations, the reliance on empirical compact models, such as the Berkeley Short-channel IGFET Model (BSIM) and neural compact models, introduces approximations that may significantly diverge from actual physical phenomena. Identifying and filtering out unphysical behaviors and erroneous simulation outcomes is a challenging task, traditionally requiring extensive expert involvement and incurring high costs. In response, we introduce FuncAnoDe, a novel neural operator for unsupervised functional anomaly detection in semiconductor simulation datasets. FuncAnoDe is the first to offer deep learning-based function-level anomaly detection without manual expert intervention. Its function-level encoder-decoder architecture enables applications across a diverse range of device parameters and simulations, ensuring scalability and high accuracy in identifying physically implausible parameter configurations. Our evaluations were conducted through complex capacitance-voltage (C-V) curve analysis, and FuncAnoDe demonstrated its effectiveness in anomaly detection by achieving a 100.00% accuracy without reliance on manual labeling. FuncAnoDe provides a methodological advancement that enhances the precision, reliability, and efficiency of semiconductor design and simulation workflows.