As device sizes are downscaled to nanometer, Random Telegraph Noise (RTN) becomes dominant. It is indispensable to accurately estimate the effect of RTN. We propose an RTN simulation method for analog circuits. It is based on the charge trapping model. The RTN-induced threshold voltage fluctuation are replicated to attach a variable DC voltage source to the gate of a MOSFET by using Verilog-AMS. In recent deca-nanometer processes, high-k (HK) materials are used in gate dielectrics to decrease the leakage current. We must consider the defect distribution characteristics both in HK and interface layer (IL). This RTN model can be applied to the bimodal model which includes characteristics of the HK and IL dielectrics. We confirm that the drain current of MOSFETs temporally fluctuates in circuit-level simulations. The fluctuations of RTN are different in MOSFETs. RTN affects the frequency characteristics of ring oscillators (ROs). The distribution of RTN-induced frequency fluctuations has a long-tail in a HK process. The RTN model applied to the bimodal can replicate a long-tail distribution. Our proposed method can estimate the temporal impact of RTN including multiple transistors.

We analyze the correlation between BTI (Bias Temperature Instability) -induced degradations and process variations. Those reliability issues are correlated. BTI is one of the most significant aging-degradations on LSIs. Threshold voltages of MOSFETs increase with time when biases stress their gates. It shows a strong effect of BTI on highly scaled LSIs in the same way as the process variations. The accurate prediction of the combinational effects is indispensable. We should analyze both aging-degradations and process variations of MOSFETs to explain the correlation. We measure frequencies of ROs (Ring Oscillators) of 65-nm process test circuits on two types of LSIs, ASICs and FPGAs. There are 98 and 837 ROs on our ASICs and FPGAs respectively. The frequencies of ROs follow gaussian distributions. We describe the highest frequency group as the “fast” conditon, the average group as the “typical” conditon and the lowest group as the “slow” conditon. We measure the aging-degradations of the ROs of the three conditions on the accelerated test. The degradations can be approximated by logarithmic function of stress time. The degradation at the “fast” condition has a higher impact on the frequency than the “slow” one. The correlation coefficient is 0.338. In this case, we can define a smaller design margin for BTI-induced degradations than that without considering the correlation because the degradation at the “slow” conditon is smaller than the average and the fast.