Statistical simulation of self-heating induced variability and reliability with application to Nanosheet-FETs based SRAM

Abstract We propose a new methodology to analyze self-heating induced variability and reliability in digital circuits during random operation. The arbitrary power waveform (APW) self-heating model is used for the self-heating evaluation with the input sequences generated by the power waveform generator (PWG). Based on the proposed method, the variability and HCI as well as BTI degradation correlated with self-heating effect (SHE) in Nanosheet-FETs based SRAM are investigated. It reveals that reducing the bit-line capacitance ( C BL ) and the minimum differential voltage between bit lines ( Δ V BLB MIN ) can suppress SHE and the temperature variation during operation of SRAM. The results also show that it is essential to take the self-heating variation into account for circuit design and reliability prediction.