Phase change memory(PCM)has reached the level of mass production.The first step in mass production is determining the proper pulse conditions of high-resistance(HR)and low-resistance(LR)states to realize the best perf...Phase change memory(PCM)has reached the level of mass production.The first step in mass production is determining the proper pulse conditions of high-resistance(HR)and low-resistance(LR)states to realize the best performance of PCM chips on the basis of longer endurance characteristics.However,due to the neglect of each of the relations as well as the square term of each relationship for pulse conditions,the standard screening method for pulse conditions cannot accurately det ermine the optimal pulse conditions.A new statistical prediction method based on regression analysis is presented in this work.The method can model and predict the optimal pulse conditions of PCM chips on the basis of longer endurance characteristics.In the method,the parameter est imates,model equations and surface plot are genera ted by the least-mean-square(LMS)method for the regression analysis;the prediction model is established by monitoring the distributions of the resistance values collected from a 4 Kbit block of the 4 Mbit PCM test chips in 40 nm complementary metal oxide semiconductor(CMOS)process.展开更多
Phase change memory (PCM) cells based on Ge2Sb2Te5 were synthesized and investigated. Currentvoltage measurements demonstrated different final resistances. Transmission electron microscopy (TEM),high resolution electr...Phase change memory (PCM) cells based on Ge2Sb2Te5 were synthesized and investigated. Currentvoltage measurements demonstrated different final resistances. Transmission electron microscopy (TEM),high resolution electron microscopy (HREM) and the energy dispersive X-ray spectroscopy (EDS) analyses were used to characterize the microstructures of the PCM cells. The architectures,structures and defects in the cells including the deposited elemental distributions and the interfacial structures between electrodes and barrier layers were studied in detail.展开更多
基金the National Key Research and Development Program of China(Nos.2017YFA0206101,2017YFB0701703,2017YFA0206104,2017YFB0405601 and 2018YFB0407500)the National Natural Science Foundation of China(Nos.61874178 and 61874129)+1 种基金the Project of the Science and Technology Council of Shanghai(No.17DZ2291300)the Shanghai Sailing Program(No.19YF1456100)。
文摘Phase change memory(PCM)has reached the level of mass production.The first step in mass production is determining the proper pulse conditions of high-resistance(HR)and low-resistance(LR)states to realize the best performance of PCM chips on the basis of longer endurance characteristics.However,due to the neglect of each of the relations as well as the square term of each relationship for pulse conditions,the standard screening method for pulse conditions cannot accurately det ermine the optimal pulse conditions.A new statistical prediction method based on regression analysis is presented in this work.The method can model and predict the optimal pulse conditions of PCM chips on the basis of longer endurance characteristics.In the method,the parameter est imates,model equations and surface plot are genera ted by the least-mean-square(LMS)method for the regression analysis;the prediction model is established by monitoring the distributions of the resistance values collected from a 4 Kbit block of the 4 Mbit PCM test chips in 40 nm complementary metal oxide semiconductor(CMOS)process.
基金Supported by the National Basic Research Program of China (Grant No. 2007CB935400)Key Project of Beijing Education Committee Program (Grant No. JB102001200801)Program for New Century Excellent Talents in University (Grant No. 05009015200701)
文摘Phase change memory (PCM) cells based on Ge2Sb2Te5 were synthesized and investigated. Currentvoltage measurements demonstrated different final resistances. Transmission electron microscopy (TEM),high resolution electron microscopy (HREM) and the energy dispersive X-ray spectroscopy (EDS) analyses were used to characterize the microstructures of the PCM cells. The architectures,structures and defects in the cells including the deposited elemental distributions and the interfacial structures between electrodes and barrier layers were studied in detail.
