RESUMO
The quantum anomalous Hall (QAH) effect appears in ferromagnetic topological insulators (FMTIs) when a Dirac mass gap opens in the spectrum of the topological surface states (SSs). Unaccountably, although the mean mass gap can exceed 28 meV (or â¼320 K), the QAH effect is frequently only detectable at temperatures below 1 K. Using atomic-resolution Landau level spectroscopic imaging, we compare the electronic structure of the archetypal FMTI Cr0.08(Bi0.1Sb0.9)1.92Te3 to that of its nonmagnetic parent (Bi0.1Sb0.9)2Te3, to explore the cause. In (Bi0.1Sb0.9)2Te3, we find spatially random variations of the Dirac energy. Statistically equivalent Dirac energy variations are detected in Cr0.08(Bi0.1Sb0.9)1.92Te3 with concurrent but uncorrelated Dirac mass gap disorder. These two classes of SS electronic disorder conspire to drastically suppress the minimum mass gap to below 100 µeV for nanoscale regions separated by <1 µm. This fundamentally limits the fully quantized anomalous Hall effect in Sb2Te3-based FMTI materials to very low temperatures.
RESUMO
Trees are used by animals, humans and machines to classify information and make decisions. Natural tree structures displayed by synapses of the brain involves potentiation and depression capable of branching and is essential for survival and learning. Demonstration of such features in synthetic matter is challenging due to the need to host a complex energy landscape capable of learning, memory and electrical interrogation. We report experimental realization of tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses. This demonstration represents physical realization of ultrametric trees, a concept from number theory applied to the study of spin glasses in physics that inspired early neural network theory dating almost forty years ago. We apply the tree-like memory features in spiking neural networks to demonstrate high fidelity object recognition, and in future can open new directions for neuromorphic computing and artificial intelligence.
