- 标题
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[IEEE 2018 20th International Conference on Transparent Optical Networks (ICTON) - Bucharest (2018.7.1-2018.7.5)] 2018 20th International Conference on Transparent Optical Networks (ICTON) - Transparent Conducting Oxides for Optoelectronics and Biosensing Applications
摘要： Transparent conducting oxides have excellent electrical and optical properties that can be exploited to enhance the performance of devices for a large variety of applications such as integrated optoelectronics, biosensing, light detection or resistive memories. In addition, they have also shown the ability to be integrated in silicon CMOS devices and therefore the potential for mass production. In this work, we will focus on ITO and ZnO for different application fields of integrated optoelectronics, memristors and biosensing.
关键词： biosensing,memristors,transparent conducting oxides,indium tin oxide,zinc oxide,switching
更新于2025-09-23 15:22:29
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A Robust Artificial Synapse Based on Organic Ferroelectric Polymer
摘要： Memristors with history-dependent resistance are considered as artificial synapses and have potential in mimicking the massive parallelism and low-power operation existing in the human brain. However, the state-of-the-art memristors still suffer from excessive write noise, abrupt resistance variation, inherent stochasticity, poor endurance behavior, and costly energy consumption, which impedes massive neural architecture. A robust and low-energy consumption organic three-terminal memristor based on ferroelectric polymer gate insulator is demonstrated here. The conductance of this memristor can be precisely manipulated to vary between more than 1000 intermediate states with the highest OFF/ON ratio of ≈104. The quasicontinuous resistive switching in the MoS2 channel results from the ferroelectric domain dynamics as confirmed unambiguously by the in situ real-time correlation between dynamic resistive switching and polarization change. Typical synaptic plasticity such as long-term potentiation and depression (LTP/D) and spike-timing dependent plasticity (STDP) are successfully simulated. In addition, the device is expected to experience 1 × 109 synaptic spikes with an ultralow energy consumption for each synaptic operation (less than 1 fJ, compatible with a bio-synaptic event), which highlights its immense potential for the massive neural architecture in bioinspired networks.
关键词： artificial synapses,organic,PVDF,ferroelectric,memristors
更新于2025-09-23 15:21:21
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Truly Concomitant and Independently Expressed Short- and Long-Term Plasticity in a Bi <sub/>2</sub> O <sub/>2</sub> Se-Based Three-Terminal Memristor
摘要： Concomitance of diverse synaptic plasticity across different timescales produces complex cognitive processes. To achieve comparable cognitive complexity in memristive neuromorphic systems, devices that are capable of emulating short-term (STP) and long-term plasticity (LTP) concomitantly are essential. In existing memristors, however, STP and LTP can only be induced selectively because of the inability to be decoupled using different loci and mechanisms. In this work, the first demonstration of truly concomitant STP and LTP is reported in a three-terminal memristor that uses independent physical phenomena to represent each form of plasticity. The emerging layered material Bi2O2Se is used for memristors for the first time, opening up the prospects for ultrathin, high-speed, and low-power neuromorphic devices. The concerted action of STP and LTP allows full-range modulation of the transient synaptic efficacy, from depression to facilitation, by stimulus frequency or intensity, providing a versatile device platform for neuromorphic function implementation. A heuristic recurrent neural circuitry model is developed to simulate the intricate “sleep–wake cycle autoregulation” process, in which the concomitance of STP and LTP is posited as a key factor in enabling this neural homeostasis. This work sheds new light on the development of generic memristor platforms for highly dynamic neuromorphic computing.
关键词： Bi2O2Se,hybrid density functional calculations,long-term plasticity,short-term plasticity,three-terminal memristors
更新于2025-09-23 15:21:01
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Memristive Behavior Enabled by Amorphousa??Crystalline 2D Oxide Heterostructure
摘要： The emergence of memristive behavior in amorphous–crystalline 2D oxide heterostructures, which are synthesized by atomic layer deposition (ALD) of a few-nanometer amorphous Al2O3 layers onto atomically thin single-crystalline ZnO nanosheets, is demonstrated. The conduction mechanism is identified based on classic oxygen vacancy conductive channels. ZnO nanosheets provide a 2D host for oxygen vacancies, while the amorphous Al2O3 facilitates the generation and stabilization of the oxygen vacancies. The conduction mechanism in the high-resistance state follows Poole–Frenkel emission, and in the low-resistance state is fitted by the Mott–Gurney law. From the slope of the fitting curve, the mobility in the low-resistance state is estimated to be ≈2400 cm2 V?1 s?1, which is the highest value reported in semiconductor oxides. When annealed at high temperature to eliminate oxygen vacancies, Al is doped into the ZnO nanosheet, and the memristive behavior disappears, further confirming the oxygen vacancies as being responsible for the memristive behavior. The 2D heterointerface offers opportunities for new design of high-performance memristor devices.
关键词： zinc oxide,2D heterostructures,memristors,atomic layer deposition,oxygen vacancies
更新于2025-09-23 15:19:57
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Quantum Memristors in Frequency-Entangled Optical Fields
摘要： A quantum memristor is a passive resistive circuit element with memory, engineered in a given quantum platform. It can be represented by a quantum system coupled to a dissipative environment, in which a system–bath coupling is mediated through a weak measurement scheme and classical feedback on the system. In quantum photonics, such a device can be designed from a beam splitter with tunable reflectivity, which is modified depending on the results of measurements in one of the outgoing beams. Here, we show that a similar implementation can be achieved with frequency-entangled optical fields and a frequency mixer that, working similarly to a beam splitter, produces state superpositions. We show that the characteristic hysteretic behavior of memristors can be reproduced when analyzing the response of the system with respect to the control, for different experimentally attainable states. Since memory effects in memristors can be exploited for classical and neuromorphic computation, the results presented in this work could be a building block for constructing quantum neural networks in quantum photonics, when scaling up.
关键词： quantum photonics,quantum neural networks,memristive systems,quantum memristors
更新于2025-09-23 15:19:57
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[IEEE 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Sydney, Australia (2018.11.10-2018.11.17)] 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC) - Laser-Based Scintillator Crystal Emulator for Optical Testing of SiPM Readout Technologies
摘要： Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms.
关键词： winner-take-all,probabilistic learning,stochastic computing,Neuromorphic systems,probabilistic inference,spiking neurons,stochastic memristors
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - A Generalized Analytic Model to Tailor Back Contact Design of Bifacial PERC-type Cu(In,Ga)Se <sub/>2</sub> solar cells
摘要： Information security has emerged as an important system and application metric. Classical security solutions use algorithmic mechanisms that address a small subset of emerging security requirements, often at high-energy and performance overhead. Further, emerging side-channel and physical attacks can compromise classical security solutions. Hardware security solutions overcome many of these limitations with less energy and performance overhead. Nanoelectronics-based hardware security preserves these advantages while enabling conceptually new security primitives and applications. This tutorial paper shows how one can develop hardware security primitives by exploiting the unique characteristics such as complex device and system models, bidirectional operation, and nonvolatility of emerging nanoelectronic devices. This paper then explains the security capabilities of several emerging nanoelectronic devices: memristors, resistive random-access memory, contact-resistive random-access memory, phase change memories, spin torque-transfer random-access memory, orthogonal spin transfer random access memory, graphene, carbon nanotubes, silicon nanowire field-effect transistors, and nanoelectronic mechanical switches. Further, the paper describes hardware security primitives for authentication, key generation, data encryption, device identification, digital forensics, tamper detection, and thwarting reverse engineering. Finally, the paper summarizes the outstanding challenges in using emerging nanoelectronic devices for security.
关键词： Emerging technologies,memristors,hardware security,PCMs,physical unclonable functions
更新于2025-09-19 17:13:59
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[IEEE 2019 Device Research Conference (DRC) - Ann Arbor, MI, USA (2019.6.23-2019.6.26)] 2019 Device Research Conference (DRC) - Waveguide Uni-Traveling-Carrier Photodiodes for mmW Signal Generation: Space-Charge Impedance and Efficiency Limitations
摘要： Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms.
关键词： Neuromorphic systems,winner-take-all,stochastic computing,probabilistic learning,probabilistic inference,spiking neurons,stochastic memristors
更新于2025-09-19 17:13:59
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[IEEE 2019 International Vacuum Electronics Conference (IVEC) - Busan, Korea (South) (2019.4.28-2019.5.1)] 2019 International Vacuum Electronics Conference (IVEC) - Notice of Removal: Oversized Smooth-Walled Horn Feeder for High Power Millimeter Wave
摘要： Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms.
关键词： winner-take-all,probabilistic learning,stochastic computing,Neuromorphic systems,probabilistic inference,spiking neurons,stochastic memristors
更新于2025-09-16 10:30:52
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Environmentally Robust Memristor Enabled by Lead‐Free Double Perovskite for High‐Performance Information Storage
摘要： Memristors are emerging as a rising star of new computing and information storage techniques. However, the practical applications are severely challenged by their instability toward harsh conditions, including high moisture, high temperatures, fire, ionizing irradiation, and mechanical bending. In this work, for the first time, lead-free double perovskite Cs2AgBiBr6 is utilized for environmentally robust memristors, enabling highly efficient information storage. The memory performance of the typical indium-tin-oxide/Cs2AgBiBr6/Au sandwich-like memristors is retained after 1000 switching cycles, 105 s of reading, and 104 times of mechanical bending, comparable to other halide perovskite memristors. Most importantly, the memristive behavior remains robust in harsh environments, including humidity up to 80%, temperatures as high as 453 K, an alcohol burner flame for 10 s, and 60Co γ-ray irradiation for a dosage of 5 × 105 rad (SI), which is not achieved by any other memristors and commercial flash memory techniques. The realization of an environmentally robust memristor from Cs2AgBiBr6 with a high memory performance will inspire further development of robust electronics using lead-free double perovskites.
关键词： lead free,resistive random access memory,memory,double perovskite,memristors
更新于2025-09-11 14:15:04