Emerging analog resistive random access memory (RRAM) based on HfOx is an attractive device for non-von Neumann neuromorphic computing systems. The differences in temperature dependent conductance drift among cells hamper computing accuracy, characterized by the statistical distribution of temperature coefficient(Tα). A compact model was presented in order to investigate the statistical distribution of Tα under different resistance states. Based on this model, the physical mechanism of thermal instability of cells with a positive Tα was elucidated. Furthermore, this model can also effectively evaluate the impact of conductance distribution of different levels under various temperatures in artificial neural networks (ANN). An approach incorporating the optimized conductance range selection and the current compensation scheme was proposed to reduce the impacts of the distribution of Tα. The simulation results showed that recognition accuracy was improved from 79.8% to 89.6% for the application of MNIST handwriting digits classification with a two-layer perceptron at 400K after adopting the proposed optimization method.
In order to estimate the state of health (SOH) of each cell in the battery pack, polarization resistance and ohmic resistance were analyzed in the aging process of the battery pack. Accelerating aging test for the battery was done, quantitative relationship between the ohmic resistance and the capacity aging was obtained, a method of relative state of health (RSOH) evaluation was proposed accordingly, Experiments on the LiFePO4 battery pack which is connected in series by 100 cells have been taken, the experimental results show that the evaluation method of RSOH can evaluate the cells SOH accurately and is not limited by the operating conditions.
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