相关论文: Phase Change Memory by GeSbTe Electrodeposition in…

Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler for reliable AI systems. However, conventional hardware…

Many key electronic technologies (e.g., large-scale computing, machine learning, and superconducting electronics) require new memories that are fast, reliable, energy-efficient, and of low-impedance at the same time, which has remained a…

Compute in-memory (CIM) is a promising technique that minimizes data transport, the primary performance bottleneck and energy cost of most data intensive applications. This has found wide-spread adoption in accelerating neural networks for…

Scalability and performance of current flash memories can be improved substantially by replacing the floating poly-Si gate by a layer of Si dots. This multi-dot layer can be fabricated CMOS-compatibly in very thin gate oxide by ion beam…

Electrochemical random-access memory (ECRAM) is a recently developed and highly promising analog resistive memory element for in-memory computing. One longstanding challenge of ECRAM is attaining retention time beyond a few hours. This…

Multi-component chalcogenides, such as quasi-binary GeTe-Sb$_{2}$Te$_{3}$ alloys, are widely used in optical data storage media in the form of rewritable optical discs. Ge$_{2}$Sb$_{2}$Te$_{5}$ (GST) in particular has proven to be one of…

The complementary field-effect transistors (CFETs), featuring vertically stacked n/p-FETs, enhance integration density and significantly reduce the area of standard cells such as static random-access memory (SRAM). However, the advantage of…

This paper studies the design and dynamic modelling of a novel thermal energy storage (TES) system combined with a refrigeration system based on phase change materials (PCM). Cold-energy production supported by TES systems is a very…

We present a hybrid device platform for creating an electrically reconfigurable metasurface formed by the integration of plasmonic nanostructures with phase-change material germanium antimony telluride (GST). By changing the phase of GST…

Ferroelectrics offer a promising materials platform to realize energy-efficient non-volatile memory technology with the FeFET-based implementations being one of the most area-efficient ferroelectric memory architectures. However, the FeFET…

We have measured the critical phase change conditions induced by electrical pulses in Ge2Sb2Te5 nanopillar phase change memory devices by constructing a comprehensive resistance map as a function of pulse parameters (width, amplitude and…

Pr1-xCaxMnO3 (PCMO) based resistance random access memory (RRAM) is attractive in large scale memory and neuromorphic applications as it is non-filamentary, area scalable and has multiple resistance states along with excellent endurance and…

In the past decades, silicon photonic integrated circuits (PICs) have been considered as a promising approach to solve the bandwidth bottleneck in optical communications and interconnections. Despite significant advances, large-scale PICs…

In-memory computing is a promising alternative to traditional computer designs, as it helps overcome performance limits caused by the separation of memory and processing units. However, many current approaches struggle with unreliable…

Phase change materials (PCMs) play a pivotal role in the development of advanced thermal devices due to their reversible phase transitions, which drastically modify their thermal and optical properties. In this study, we present an…

Structural colors generated due to light scattering from static all-dielectric metasurfaces have successfully enabled high-resolution, high-saturation, and wide-gamut color printing applications. Despite recent advances, most demonstrations…

Resistive Random Access Memory (RRAM) and Phase Change Memory (PCM) devices have been popularly used as synapses in crossbar array based analog Neural Network (NN) circuit to achieve more energy and time efficient data classification…

Adaptable, reconfigurable and programmable are key functionalities for the next generation of silicon-based photonic processors, neural and quantum networks. Phase change technology offers proven non-volatile electronic programmability,…

Adding thermal conductivity enhancements to increase thermal power in solid-liquid phase-change thermal energy storage modules compromises volumetric energy density and often times reduces the mass and volume of active phase change material…

Chalcogenide phase-change materials (PCMs) are regarded as the leading candidate for storage-class non-volatile memory and neuro-inspired computing. Recently, using the $TiTe_2$/$Sb_2Te_3$ material combination, a new framework -…