Complementary metal–oxide–semiconductor (CMOS)-based neural architectures and memristive devices containing many artificial synapses are promising technologies that are being developed for pattern recognition and machine learning. However, the volatility and design complexity of traditional CMOS architectures, and the trade-off between the operating time and power consumption of conventional memristive devices, have tended to impede the path to achieve the interconnectivity/compactness and information density of the brain using either approach. Here, by developing a nanoscale deposit-only-metal-electrode-fabrication-based uniform-partial-state-transition-facilitated approach, we demonstrate a fast artificial synapse with a Rapid-operating-time, Intermediate-bias-range, Multiple-states, and Several-synaptic-functions (RIMS) synapse, implemented using deposit-only, nanopillar-based Ge2Sb2Te5-type memristive devices. A previously unconsidered, fast, paired-pulse facilitation/depression using ∼50 ns spikes with an ∼1 µs inter-spike interval within an ∼1 V range and with a low-energy consumption of ∼1.8 pJ per paired-spike as well as a previously inaccessible multi-state, rapid long-term potentiation/depression with ∼15 distinct states using ∼50 ns spikes within a 0.7/1.4 V range was achieved. Fast spike-timing-dependent plasticity using ∼50 ns spikes with an ∼1 µs inter-spike interval within a 1.3 V range was also achieved. Electro-thermal simulations reveal a uniform-partial-state-transition-facilitated variation in conductance states. This artificial synapse, equipped with a nanoscale deposit-only-metal-electrode-fabrication-based uniform-partial-state-transition-facilitated framework, shows the potential for a substantial overall performance improvement in artificial-intelligence tasks.
There is an ever-increasing demand for next-generation devices that do not require passwords and are impervious to cloning. For traditional hardware security solutions in edge computing devices, inherent limitations are addressed by physical unclonable functions (PUF). However, realizing efficient roots of trust for resource constrained hardware remains extremely challenging, despite excellent demonstrations with conventional silicon circuits and archetypal oxide memristor-based crossbars. An attractive, down-scalable approach to design efficient cryptographic hardware is to harness memristive materials with a large-degree-of-randomness in materials state variations, but this strategy is still not well understood. Here, the utilization of high-degree-of-randomness amorphous (A) state variations associated with different operating conditions via thermal fluctuation effects is demonstrated, as well as an integrated framework for in memory computing and next generation security primitives, viz.
Energy-efficient compact alternatives to fully digital computing strategies could be achieved by implementations of artificial neural networks (ANNs) that borrow analog techniques. In-memory computing based on crossbar device architectures with memristive materials systems that execute, in an analog way, multiply-and-accumulate operations prevalent in ANN is a notable example. Ferroelectric (FE) materials are promising candidates for achieving ANN thanks to their excellent down-scalability, improved electrical control, and high energy efficiency. However, it remains challenging to develop a crossbar device architecture using FE materials. The difficulty stems from decreasing the leakage current of FE hardware and, simultaneously, reducing the film thickness for achieving compact systems. Here, we have performed density-functional-theory calculations to investigate the electronic, energy-based, and structural signatures of wurtzite FE material Al0.75Sc0.25N with a nitrogen vacancy ( VN) in different charge states. We find that VN can introduce two defect states, viz., the singlet state above the valence band maximum (VBM) and a triplet state below the conduction band minimum in wurtzite AlScN models. The calculations reveal that the group of transition levels E3+/2+/ E2+/1+ with small formation energies occur at ∼0.78/1.03 eV above the VBM in the wurtzite AlScN with a relaxed configuration, which may shift by a large degree to lower energy levels if atoms surrounding the defect are not fully relaxed. Theoretical studies elucidate the vacancy-enhanced increase in the leakage current utilizing large AlScN supercells. These findings render atomistic insights that can provide a path forward for the design of next-generation portable low-power electronic systems.
Developing novel nanostructures and advanced nanotechnologies for cancer treatment has attracted ever-increasing interest. Electrothermal therapy offers many advantages such as high efficiency and minimal invasiveness, but finding a balance between increasing stability of the nanostructure state and, at the same time, enhancing the nanostructure biodegradability presents a key challenge. Here, we modulate the biodegradation process of two-dimensional-material-based nanostructures by using polyethylene glycol (PEG) via nanostructure disrupt-and-release effects. We then demonstrate the development of a previously unreported alternating current (AC) pulse WS 2 /PEG nanostructure system for enhancing therapeutic performance. A decrease in cell viability of ∼42% for MCF-7 cells with WS 2 /PEG was achieved, which is above an average of ∼25% for current electrothermal-based therapeutic methods using similar energy densities, as well as degradation time of the WS 2 of ∼1 week, below an average of ∼3.5 weeks for state-of-the-art nanostructure-based systems in physiological media. Moreover, the incubation time of MCF-7 cells with WS 2 /PEG reached ∼24 h, which is above the average of ∼4.5 h for current electrothermal-based therapeutic methods and with the use of the amount of time harnessed to incubate the cells with nanostructures before applying a stimulus as a measure of incubation time. Material characterizations further disclose the degradation of WS 2 and the grafting of PEG on WS 2 surfaces. These WS 2 -based systems offer strong therapeutic performance and, simultaneously, maintain excellent biodegradability/biocompatibility, thus providing a promising route for the ablation of cancer.
Traditional physical-based models have generally been used to model the resistive-switching behavior of resistive-switching memory (RSM). Recently, vacancy-based conduction-filament (CF) growth models have been used to model device characteristics of a wide range of RSM devices. However, few have focused on learning the other-device-parameter values (e.g., low-resistance state, high-resistance state, set voltage, and reset voltage) to compute the compliance-current (CC) value that controls the size of CF, which can influence the behavior of RSM devices. Additionally, traditional CF growth models are typically physical-based models, which can show accuracy limitations. Machine learning holds the promise of modeling vacancy-based CF growth by learning other-device-parameter values to compute the CC value with excellent accuracy via examples, bypassing the need to solve traditional physical-based equations. Here, we sidestep the accuracy issues by directly learning the relationship between other-device-parameter values to compute the CC values via a data-driven approach with high accuracy for test devices and various device types using machine learning. We perform the first modeling with machine-learned device parameters on aluminum-nitride-based RSM devices and are able to compute the CC values for nitrogen-vacancy-based CF growth using only a few RSM device parameters. This model may now allow the computation of accurate RSM device parameters for realistic device modeling.
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