Thermal Transport Across Graphene Step Junctions

Step junctions are often present in layered materials, i.e. where single-layer regions meet multi-layer regions, yet their effect on thermal transport is not understood to date. Here, we measure heat flow across graphene junctions (GJs) from monolayer to bilayer graphene, as well as bilayer to four-layer graphene for the first time, in both heat flow directions. The thermal conductance of the monolayer-bilayer GJ device ranges from ~0.5 to 9.1x10^8 Wm-2K-1 between 50 K to 300 K. Atomistic simulations of such GJ device reveal that graphene layers are relatively decoupled, and the low thermal conductance of the device is determined by the resistance between the two dis-tinct graphene layers. In these conditions the junction plays a negligible effect. To prove that the decoupling between layers controls thermal transport in the junction, the heat flow in both directions was measured, showing no evidence of thermal asymmetry or rectification (within experimental error bars). For large-area graphene applications, this signifies that small bilayer (or multilayer) islands have little or no contribution to overall thermal transport

Spatially resolved thermometry of micro- and nano- devices using scanning thermal microscopy

Resumen del trabajo presentado en la 16th International Conference on Nanostructured Materials NANO 2022, celebrada en Sevilla (España), del 6 al 10 de junio de 2022Self-heating and localized temperatures play an important role in the principle of operation of nano- and micro-scale devices. On the one hand, heating in transistor devices affects the mobility of the carriers, limiting device performance and lifetime. On the other hand, energy dissipation in memory devices is connected to some drawbacks, like reliability and energy efficiency. Understanding the energy dissipation mechanisms is therefore essential for the evaluation, design and optimization of our electronic devices. Optical techniques, like infrared (IR) or Raman thermometry, can be used to obtain thermal maps of devices but their spatial resolution is diffraction limited, i.e., ~5 µm and ~0.5 µm respectively. Scanning thermal microscopy (SThM) is a scanning probe microscopy technique that allows thermal maps of devices with nanoscale resolution (~50 nm). Therefore, SThM is a promising tool for determining local hot spots and self-heating of different types of devices. In this work, we show how SThM can be employed for the characterization of heat dissipation in nanoelectronics. Our SThM uses a thermo-resistive probe whose electrical resistance varies with temperature. This probe can be used as a nanoscale sensor to map the temperature of devices locally. First, we present challenges associated with the calibration of this probe, which are key to obtaining quantitative measurements of device temperatures. Second, we show how a calibrated SThM system can be used to gain knowledge of the energy dissipation of memory devices. We focus on resistive random access (RRAM) and phase change (PCM) memory devices, which show promise for applications such as non-volatile memory and neuromorphic computing. The SThM thermal maps show the filamentary heating from RRAM devices as well as the Joule heated PCM device, displaying local temperature features. These maps provide insights into device operation, showing how the energy dissipates and offering new routes for developing more efficient switching mechanism

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Localized Heating and Switching in MoTe2 -Based Resistive Memory Devices

Two-dimensional (2D) materials have recently been incorporated into resistive memory devices due to their atomically thin nature, but their switching mechanism is not yet well understood. Here we study bipolar switching in MoTe2-based resistive memory of varying thickness and electrode area. Using scanning thermal microscopy (SThM), we map the surface temperature of the devices under bias, revealing clear evidence of localized heating at conductive “plugs” formed during switching. The SThM measurements are correlated to electro-thermal simulations, yielding a range of plug diameters (250 to 350 nm) and temperatures at constant bias and during switching. Transmission electron microscopy images reveal these plugs result from atomic migration between electrodes, which is a thermally-activated process. However, the initial forming may be caused by defect generation or Te migration within the MoTe2. This study provides the first thermal and localized switching insights into the operation of such resistive memory, and demonstrates a thermal microscopy technique that can be applied to a wide variety of traditional and emerging memory devices

Thermal mapping of nanoscale filamentary hot spots in Resistive Memory Devices

Trabajo presentado en la International 16th Conference on Nanostructured Materials, NANO 2022, celebrada en Sevilla (España), del 6 al 10 de junio de 2022Resistive random-access memories (RRAM) hold promise for developing future information technologies with ultra-high storage densities to process unprecedented amounts of data. RRAM operation relies on the formation (set) and rupture (reset) of nanoscale conductive filaments (CFs) with diameters as low as few nanometers in the switching layer. These filaments carry enormous power densities (>1013 W/cm3) that result in high localized temperatures. The heating behavior of these filaments lies at the heart of this technology but little is known experimentally about it. Understanding the temperature of nanoscale filamentary hot spots is key to developing more energy-efficient devices and avoiding thermal cross-talk in future dense RRAM arrays. In this work we report the first thermal measurements of single filament switching in RRAM. For that purpose, we use calibrated scanning thermal microscopy (SThM). This technique allows thermal metrology at sub-50 nm dimensions. We use SThM to locate such filaments in HfO2 RRAM devices with nanoscale resolution and determine the hot spot temperature at the top surface during operation. Then, we match the temperature profile with finite element simulations, using the filament diameter and the thermal boundary conductance at the filament-top electrode interface as fitting parameters, assuming diffusive transport in the filament. We use both conventional (metal) and novel (graphene) top electrodes to assess the effect of heat spreading in memory devices. Our study reveals that a CF of 4 nm with TiN top electrode and 13 nm with single layer graphene as top electrode at power ~100 ¿W can lead to a temperature rise as high as ~1100 K above ambient temperature. We solve a fundamental yet elusive problem that has existed in the data storage community for two decades, i.e. imaging the spatial extent and temperature of the filament operation in metal-oxide-based RRAM. Based on the information provided by the SThM measurement, thermal engineering approaches could be applied to develop energy efficient devices. As an example, one could choose electrodes with a thermal boundary conductance that is low at the filament electrode interface to confine the CF heating, and high at the surrounding oxide interfaces to minimize the lateral heat spreading. Overall, these results advance our knowledge of RRAM engineering for digital storage vs analog computing, our understanding of breakdown in insulators, and showcase a unique application of the SThM technique with ramifications much beyond memory technology

Ultrahigh thermal isolation across heterogeneously layered two-dimensional materials

Heterogeneous integration of nanomaterials has enabled advanced electronics and photonics applications. However, similar progress has been challenging for thermal applications, in part due to shorter wavelengths of heat carriers (phonons) compared to electrons and photons. Here, we demonstrate unusually high thermal isolation across ultrathin heterostructures, achieved by layering atomically thin two-dimensional (2D) materials. We realize artificial stacks of monolayer graphene, MoS2, and WSe2 with thermal resistance greater than 100 times thicker SiO2 and effective thermal conductivity lower than air at room temperature. Using Raman thermometry, we simultaneously identify the thermal resistance between any 2D monolayers in the stack. Ultrahigh thermal isolation is achieved through the mismatch in mass density and phonon density of states between the 2D layers. These thermal metamaterials are an example in the emerging field of phononics and could find applications where ultrathin thermal insulation is desired, in thermal energy harvesting, or for routing heat in ultracompact geometries