Revolutionizing TCAD Simulations with Universal Device Encoding and Graph Attention Networks
This work addresses the need for faster and more efficient electronic design automation at the device level for semiconductor researchers, representing an incremental advancement by integrating AI into existing TCAD workflows.
The paper tackles the challenge of simulating semiconductor devices in TCAD by proposing a universal graph encoding scheme and a graph attention network (RelGAT) for surrogate modeling, achieving accurate current-voltage predictions with unspecified numerical improvements.
An innovative methodology that leverages artificial intelligence (AI) and graph representation for semiconductor device encoding in TCAD device simulation is proposed. A graph-based universal encoding scheme is presented that not only considers material-level and device-level embeddings, but also introduces a novel spatial relationship embedding inspired by interpolation operations typically used in finite element meshing. Universal physical laws from device simulations are leveraged for comprehensive data-driven modeling, which encompasses surrogate Poisson emulation and current-voltage (IV) prediction based on drift-diffusion model. Both are achieved using a novel graph attention network, referred to as RelGAT. Comprehensive technical details based on the device simulator Sentaurus TCAD are presented, empowering researchers to adopt the proposed AI-driven Electronic Design Automation (EDA) solution at the device level.