A new characterization model of FinFET self-heating effect based on FinFET characteristic parameter

Abstract

The characterization of the self-heating effect (SHE) has been an important research topic in advanced technology, but the existing characterizations are few and the characterization process is relatively complex. In this research, a SHE characterization model is established based on the relationship between output transconductance variation ($∆g_m$), gate source voltage (V_GS) and temperature variation ($∆T$) caused by SHE through machine learning, and then the model is validated by theoretical analyses and experimental simulation. The characterization model is capable of directly calculating the $∆T$ caused by SHE during I - V testing and simplifying the SHE characterization steps while ensuring characterization accuracy ($∆T$ difference < 1 °C), thus saving costs. It is also found that the model can expand the characterization range (V_GS: 0.3–0.7 V) of SHE and conducts quantitative characterization with model calculation under different V_GS, realizing a high characterization resolution of V_GS: 0.01 V. The circuit level application proves that the method can be effectively applied to the characterization of the SHE and solves the problem of the characterization of the circuit level SHE.