Like other deep learning problems, critical features are critical to enable effective estimation of Remaining Useful Lifetime (RUL) for power electronic devices using Neural Networks (NNs). However, these critical features are often indirectly obtained after data pre-processing, complicated either in form (high dimension) or in computation (computation-intensive pre-processing). In the paper, we suggest adding a simple direct feature, age, into the NN based RUL estimation technique. The rationale for incorporating this feature is that the device lifetime is a sum of past time (age) plus RUL. Thus it has a strong correlation to RUL. In our experiments using accelerated aging tests, we show that the new age feature enhanced Recurrent Neural Network (RNN) model can significantly improve estimation accuracy and shorten training convergence time. It also outperforms a state-of-The-Art RNN model using derived time-domain statistical features.