UC Riverside

Time dependent dielectric breakdown (TDDB) is one of the important failure mechanisms for Copper (Cu) interconnects that are used in VLSI circuits. This reliability effect becomes more severe as the space between wires is shrinking and low-k dielectric materials (low electrical and mechanical strength) are used. There are many studies and theories focusing on the physics of it. However, there is limited research from the electronics design automation (EDA) perspective on this topic, aiming to evaluate, or alleviate it from the perspective of designing a VLSI chip. This thesis compiles several studies into evaluating TDDB on the circuit level, and engineering methods that help the evaluation. The first work extends the study of a published physics model on simplified yet practical cases. It simplifies the calculation of lifetime by deriving an analytic solution and applying fitting methods. The second study proposes a new way to evaluate lifetime of a chip by extending the models of simple interconnect structures to the complete chip. This method is more robust as it focuses more on a complete chip. However, heavy dependence of finite element method (FEM) makes the flow very slow. The third study adopts machine learning methods to accelerate this slow evaluation process. The proposed method is also applicable to other similar electrostatics applications. Last but not least, the fourth study focuses on a GPU based LU factorization algorithm, which, on a broader aspect, is a universal numerical algorithm used in many different simulation applications, which can be helpful to TDDB evaluations as it can be used in FEM.