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.

准确预测组件的剩余使用寿命 (RUL) 是电子电路中的主要关注点。基于 RUL 的健康诊断在确定设备故障时间方面发挥着重要作用，作为工业应用中的早期预警。在本文中，提出了一种基于长短期记忆 (LSTM) 的回归模型，用于预测环形振荡器 (RO) 电路的 RUL，该电路利用设备中提取的最重要的电气特性。LSTM 网络能够捕捉时间序列数据中的时间依赖性，并消除传统递归神经网络 (RNN) 中遇到的梯度消失问题。从 Cadence 仿真中，利用 22 nm CMOS 技术库，已经证明，RO 频率衰减主要取决于三个主要因素，包括工作温度、电压，最重要的是器件老化参数。结果表明，90% 以上的 13 级和 21 级的 RUL 预测情况受制于 0.7 V 至 0.9 V 的电源电压变化，预测偏差最小为 2 天至 6 天。