FuncAnoDe: A Function Level Anomaly Detection in Device Simulation

Authors : Taeil Oh, Hong Chul Nam, Chanwoo Park, Hyunbo Cho

Publication : SISPAD

Date : September 2024

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.

Enhancing Interpretability of Neural Compact Models: Toward Reliable Device Modeling

Authors : Chanwoo Park, Hyunbo Cho, Jungwoo Lee

Publication : IEEE Journal of the Electron Devices Society (JEDES)

Date : June 2024

Neural Compact Models (NCMs) have emerged as a crucial tool to meet the stringent demands of Design-Technology Co-Optimization (DTCO) and to overcome the complexities and prolonged development cycles encountered in traditional compact model creation. Despite their efficiency in simulating electronic devices, a significant barrier to the widespread adoption of NCMs in the industry remains: the lack of interpretability. In the semiconductor sector, where inaccuracies or failures can lead to considerable financial consequences, it is critical to ensure that the model’s predictions are both understandable and reliable. This study aims to enhance the interpretability of NCMs used for I-V and C-V characterizations by clarifying the physical significance of latent vectors. A regularization technique is employed to disentangle features within the latent space, and interpolation is used to visualize and elucidate each dimension’s physical impact. Our approach, which offers interpretable insights into the model’s functionality, seeks to encourage broader implementation of NCMs in the industry, thus accelerating advancements in DTCO.