The degradation behavior of the Ti/HfO x bipolar resistive random access memory (RRAM) during endurance cycles, and the operational parameters, which induce the endurance failure, are studied through the two proposed stressing methods. The over-RESET energy is considered to be the key electrical parameter to induce endurance failure in the memory device. When the device suffers the over-RESET energy, a gradually reduced memory window is observed associated with endurance cycles, and the overall degradation will include two stages. The first stage can be explained by the worn filament model and is mainly due to imbalance energy between SET and RESET process. The occurrence of unusual resistance-voltage (R-V) patterns at positive and negative voltage seep in the memory device under the second stage degradation demonstrates the existence of complementary resistive switching (CRS) in the single Ti/HfO x bipolar RRAM. After analyzing the operation conditions to activate the self-CRS in memory device with one transistor-one resistor (1T-1R) configuration, the mechanism about the second stage degradation in the RRAM originated from over-RESET energy is also discussed. A mechanism based on the worn filament model and the induction of CRS is proposed to explain the endurance failure induced by over-RESET in the Ti/HfO x RRAM with 1T-1R configuration. With an appropriate RESET energy, a robust reliability for endurance cycles is expected.

In the electricity market, the real-time balance of electricity generation and consumption is a main task. In view of this, power providers usually sign contracts with their critical consumers (i.e., usually large-scale industrial companies) for managing their capacity demands. On the other hand, aggregators group commercial and residential consumers, and integrate their demands to negotiate with power providers. With a proper grouping of numerous electricity consumers, aggregators help to ensure stable electric supply, and reduce the burden of managing many consumers. In this work, we thus propose a novel data clustering approach to group complementary consumers based on their usage patterns (i.e., daily electricity consumption curves.) Furthermore, we incorporate the technique of discrete wavelet transform to speed up the clustering process. Specifically, approximations reconstructed from only a few wavelet coefficients may precisely capture the shape of original usage patterns. Experimental results based on a real dataset show that our approach is promising in practical applications.