Bulk 14-nm FinFET technology was irradiated in a heavy-ion environment (42-MeV Si ions) to study the possibility of displacement damage (DD) in scaled technology devices, resulting in drive current degradation with increased cumulative fluence. These devices were also exposed to an electron beam, proton beam, and cobalt-60 source (gamma radiation) to further elucidate the physics of the device response. Annealing measurements show minimal to no 'rebound' in the ON-state current back to its initial high value; however, the OFF-state current 'rebound' was significant for gamma radiation environments. Low-temperature experiments of the heavy-ion-irradiated devices reveal increased defect concentration as the result for mobility degradation with increased fluence. Furthermore, the subthreshold slope (SS) temperature dependence uncovers a possible mechanism of increased defect bulk traps contributing to tunneling at low temperatures. Simulation work in Silvaco technology computer-aided design (TCAD) suggests that the increased OFF-state current is a total ionizing dose (TID) effect due to oxide traps in the shallow trench isolation (STI). The significant SS elongation and ON-state current degradation could only be produced when bulk traps in the channel were added. Heavy-ion irradiation on bulk 14-nm FinFETs was found to be a combination of TID and DD effects.
In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network’s synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.
The III-nitride semiconductors have many attractive properties for field-emission vacuum electronics, including high thermal and chemical stability, low electron affinity, and high breakdown fields. Here, we report top-down fabricated gallium nitride (GaN)-based nanoscale vacuum electron diodes operable in air, with record ultralow turn-on voltages down to ∼0.24 V and stable high field-emission currents, tested up to several microamps for single-emitter devices. We leverage a scalable, top-down GaN nanofabrication method leading to damage-free and smooth surfaces. Gap-dependent and pressure-dependent studies provide new insights into the design of future, integrated nanogap vacuum electron devices. The results show promise for a new class of high-performance and robust, on-chip, III-nitride-based vacuum nanoelectronics operable in air or reduced vacuum.
Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO2-X switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.
Ashby, David; Choi, Christopher S.; Edwards, Martin A.; Talin, A.A.; White, Henry S.; Dunn, Bruce S.
It is well established that the miniaturization of batteries has not kept pace with the miniaturization of electronics. Three-dimensional (3D) batteries, which were developed with the intent of improving microbattery performance, have had limited success because of fabrication challenges and material constraints. Solid-state, 3D batteries have been particularly susceptible to these shortcomings. In this paper, we demonstrate that the incorporation of a high-conductivity, solid electrolyte is the key to achieving a nonplanar solid-state battery with high areal capacity and high power density. The model 2.5D platform used in this study is a modification of the more typical 3D configuration in that it is comprised of a cathode array of pillars (3D) and a planar (two-dimensional, 2D) anode. This 2.5D geometry exploits the use of a high-conductivity, ionogel electrolyte (10-3 S cm-1), which interpenetrates the 3D electrode array. The 2.5D battery offers high areal energy densities from the post array, while the high-conductivity, solid electrolyte enables high power densities (3.7 mWh cm-2 at 2.8 mW cm-2). The reported solid-state 2.5D device exceeds the energy and power densities of any 3D solid-state system and the derived multiphysics model provides guidance for achieving significantly higher energy and power densities.
Materials exhibiting metal-to-insulator transitions (MITs) could enable low power neuromorphic computing, but progress is hindered by insufficient mechanistic understanding. In this issue of Matter, Banerjee and colleagues describe with intricate detail a new MIT mechanism in β′-CuxV2O5, with potential applications to neuromorphic computing. Materials exhibiting metal-to-insulator transitions (MITs) could enable low power neuromorphic computing, but progress is hindered by insufficient mechanistic understanding. In this issue of Matter, Banerjee and colleagues describe with intricate detail a new MIT mechanism in β′-CuxV2O5, with potential applications to neuromorphic computing.
Terahertz (THz) technology has shown promise for several applications, but limitations in sources and detectors have prevented broader adoption. Existing THz detectors are rigid, planar, and fabricated using complex technology, making it difficult to integrate into systems. Here we demonstrate THz detectors fabricated by inkjet printing on submicrometer thick, ultraflexible substrates. By developing p- and n-type carbon nanotube inks, we achieve optically thick p–n junction and p-type devices, enabling antenna-free pixels for THz imaging. By further designing and folding the printed devices, we realize origami-inspired architectures with improved performance over single devices, achieving a noise-equivalent power of 12 nW/Hz1/2 at room temperature with no voltage bias. Our approach opens avenues for nonplanar, foldable, deployable, insertable, and retractable THz detectors for applications in nondestructive inspection.