Remaining Useful Life (RUL) Regression Using Long-Short Term Memory (LSTM) Networks

The accurate prediction of the remaining useful life (RUL) of components is a major concern in electronic circuits. The RUL-based health diagnostics plays an important role in the determination of time-of-failure of a device as an early warning in industrial applications. In this paper, a Long Short Term Memory (LSTM) based regression model is proposed for the prediction of RUL of a Ring Oscillator (RO) circuit utilizing the most essential extracted electrical features of the device. LSTM networks are capable of capturing the temporal dependencies in the time-series data and eliminating the vanishing gradient problem encountered in the conventional recurrent neural networks (RNNs). From Cadence simulations, utilizing the 22 nm CMOS technology library, it has been demonstrated that the RO frequency degradation essentially depends on three major factors including the working temperature, voltage, and most importantly, the device aging parameter. The results show that more than 90% of the cases of the RUL prediction for the 13 and 21 stage constrained under the supply voltage variation from 0.7 V to 0.9 V with the least prediction deviation of 2 days to 6 days