Data related to the nanoscale structural and compositional evolution in resistance change memories

Differential Work-Function Enabled Bifunctional Switching in Strontium Titanate Flexible Resistive Memories

Multifunctional electronic memories capable of demonstrating both analog and digital switching on-demand are extremely attractive for miniaturization of electronics without significant drain on energy consumption. Simultaneously translating functionality onto mechanically conformable platforms will further enhance their suitability. Here, we demonstrate the ability to engineer multifunctionality in strontium titanate (STO)-based resistive random-access memories (ReRAM) on a flexible polyimide platform. By utilizing different bottom electrodes of various work functions while the top electrode is fixed, differential work functions are induced in STO, to induce bipolar or complementary switching behaviors whenever required. This work-function difference-induced bifunctional switching on the flexible platform reveals a streamlined route for achieving flexible artificial neural networks, high density integration, and logic operation using a single ReRAM.

Time and rate dependent synaptic learning in neuro-mimicking resistive memories

Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the functional oxides or engineering the external programming hardware. However, high device-to-device variability in memristors induced by the electroforming process and complicated programming hardware are among the key challenges that hinder achieving biomimetic neuromorphic networks. Here, a simple hybrid complementary metal oxide semiconductor (CMOS)-memristor approach is reported to implement different synaptic learning rules by utilizing a CMOS-compatible memristor based on oxygen-deficient SrTiO3-x (STOx). The potential of such hybrid CMOS-memristor approach is demonstrated by successfully imitating time-dependent (pair and triplet spike-time-dependent-plasticity) and rate-dependent (Bienenstosk-Cooper-Munro) synaptic learning rules. Experimental results are benchmarked against in-vitro measurements from hippocampal and visual cortices with good agreement. The scalability of synaptic devices and their programming through a CMOS drive circuitry elaborates the potential of such an approach in realizing adaptive neuromorphic computation and networks.