Abstract
Shallow Trench Isolation laterally diffused metal oxide semiconductor (STI LDMOS) is a crucial device for power integrated circuits. In this article, a novel framework that integrates an optimal objective function, Bayesian Optimization (BO) algorithm and Deep Neural Network (DNN) model is proposed to fully realize automatic and optimal design of STI LDMOS devices. On the one hand, given the structure of device, the DNN model in the proposed method can provide the ultra-fast and high-accurate performance estimation including breakdown voltage (BV) and specific on-resistance (Ronsp). The experimental results demonstrate 98.68% prediction accuracy in average for both BV and Ronsp, higher than that of other machine learning (ML) algorithms. On the other hand, to target the specified value of BV and Ronsp, the proposed framework can fully automatically and optimally design the precise device structure that simultaneously achieves the target performance with the optimal figure-of-merit (FOM) of device. Compared to Technology Computer Aided Design (TCAD), there is only 0.002% error in FOM and 2.83% average error in BV and Ronsp. Moreover, the proposed framework is 4000 times more efficient than other conventional frameworks. Thus, this research provides experimental groundwork for constructing an automatic design framework for LDMOS device and opens up new opportunities for accelerating the development of LDMOS device in the future.