Reliability simulation of TMO RRAM

Combination with the RRAM analytic model with varation and the Monte Carlo simulator based on the microcosmic processes of oxygen vacancies and oxygen ion's generation, transportation and recombination, a TMO RRAM reliability simulation platform is developed to simulate and evaluate the main reliability issues of RRAM including retention, endurance, operation disturb considering the intrinsic variation. ? 2015 IEEE.EI535-5382015-Augus

Design and application of resistive switching devices for novel computing/memory architectures

Oxide-based resistive random access memory(RRAM) has been widely studied as the promising candidate for the applications of next generations of data storage and computing technologies. In this paper, we will discuss the physical mechanism and optimization design of oxide-based RRAM devices, the novel RRAM-based computing/memory unifying architectures and the applications for data storage and computing technologies.Oxide-based resistive random access memory(RRAM) has been widely studied as the promising candidate for the applications of next generations of data storage and computing technologies. In this paper, we will discuss the physical mechanism and optimization design of oxide-based RRAM devices, the novel RRAM-based computing/memory unifying architectures and the applications for data storage and computing technologies.IEEE Beijing Section

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

AbstractThe effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for &gt;60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.</jats:p