False Summit and Silent Drift: A Failure Taxonomy and Efficiency Analysis of LLM-Assisted Multiphysics Simulation in an Open-Source Framework

Abstract: Multiphysics simulation of semiconductor processes requires simultaneous command of governing equations, numerical methods, software implementation, and process physics, creating a substantial entry barrier for newcomers. We investigate whether LLM assistance and curated literature guidance can reduce this barrier during the development of a low-pressure chemical vapor deposition (LPCVD) model for graphene growth using the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE). We compare GPT and Claude across direct prompting, staged development without literature, and staged development with literature guidance. Staged development successfully produced converged simulations but revealed two recurring failure classes. We define a false summit as a simulation that converges and appears physically plausible while implementing incorrect physics, and silent drift as the undetected propagation of unverified assumptions through successive development stages. In our case study, a non-conservative species-transport formulation generated spurious methane depletion that was visually indistinguishable from genuine surface consumption, while an omitted self-limiting surface-reaction term produced physically incorrect growth behavior that the LLM repeatedly rationalized as valid. Literature guidance substantially reduced interaction burden during the most challenging development stage, decreasing prompt counts by 91% for GPT and 64% for Claude. Based on these observations, we propose an eight-category failure taxonomy, model-specific human oversight strategies, and a stage-gated workflow to improve the detectability of hidden modeling errors. The results demonstrate that LLMs can lower the barrier to multiphysics simulation development, but rigorous physical validation remains essential because apparently reasonable solutions may conceal critical defects