Three-Terminal Ovonic Threshold Switch (3T-OTS) with Tunable Threshold Voltage for Versatile Artificial Sensory Neurons.

Inspired by information processing in biological systems, sensor-combined edge-computing systems attract attention requesting artificial sensory neurons as essential ingredients. Here, we introduce a simple and versatile structure of artificial sensory neurons based on a novel three-terminal Ovonic threshold switch (3T-OTS), which features an electrically controllable threshold voltage (Vth). Combined with a sensor driving an output voltage, this 3T-OTS generates spikes with a frequency depending on an external stimulus. As a proof of concept, we have built an artificial retinal ganglion cell (RGC) by combining a 3T-OTS and a photodiode. Furthermore, this artificial RGC is combined with the reservoir-computing technique to perform a classification of chest X-ray images for normal, viral pneumonia, and COVID-19 infections, releasing the recognition accuracy of about 86.5%. These results indicate that the 3T-OTS is highly promising for applications in neuromorphic sensory systems, providing a building block for energy-efficient in-sensor computing devices.

SWIR imaging using PbS QD photodiode array sensors.

We fabricated a 1 × 10 PbS QD photodiode array with multiple stacked QD layers with high-resolution patterning using a customized photolithographic process. The array showed the average responsivity of 5.54 × 10-3 A/W and 1.20 × 10-2 A/W at 0 V and -1 V under 1310- nm short-wavelength infrared (SWIR) illumination. The standard deviation of the pixel responsivity was under 10%, confirming the uniformity of the fabrication process. The response time was 2.2 ± 0.13 ms, and the bandwidth was 159.1 Hz. A prototype 1310-nm SWIR imager demonstrated that the QD photodiode-based SWIR image sensor is a cost-effective and practical alternative for III-V SWIR image sensors.

Open-circuit voltage deficit, radiative sub-bandgap states, and prospects in quantum dot solar cells.

Quantum dot photovoltaics (QDPV) offer the potential for low-cost solar cells. To develop strategies for continued improvement in QDPVs, a better understanding of the factors that limit their performance is essential. Here, we study carrier recombination processes that limit the power conversion efficiency of PbS QDPVs. We demonstrate the presence of radiative sub-bandgap states and sub-bandgap state filling in operating devices by using photoluminescence (PL) and electroluminescence (EL) spectroscopy. These sub-bandgap states are most likely the origin of the high open-circuit-voltage (VOC) deficit and relatively limited carrier collection that have thus far been observed in QDPVs. Combining these results with our perspectives on recent progress in QDPV, we conclude that eliminating sub-bandgap states in PbS QD films has the potential to show a greater gain than may be attainable by optimization of interfaces between QDs and other materials. We suggest possible future directions that could guide the design of high-performance QDPVs.

Artificial Multimodal Neuron with Associative Learning Capabilities: Acquisition, Extinction, and Spontaneous Recovery.

Associative multimodal artificial intelligence (AMAI) has gained significant attention across various fields, yet its implementation poses challenges due to the burden on computing and memory resources. To address these challenges, researchers have paid increasing attention to neuromorphic devices based on novel materials and structures, which can implement classical conditioning behaviors with simplified circuitry. Herein, we introduce an artificial multimodal neuron device that shows not only the acquisition behavior but also the extinction and the spontaneous recovery behaviors for the first time. Being composed of an ovonic threshold switch (OTS)-based neuron device, a conductive bridge memristor (CBM)-based synapse device, and a few passive electrical elements, such observed behaviors of this neuron device are explained in terms of the electroforming and the diffusion of metallic ions in the CBM. We believe that the proposed associative learning neuron device will shed light on the way of developing large-scale AMAI systems by providing inspiration to devise an associative learning network with improved energy efficiency.

Demonstration of an energy-efficient Ising solver composed of Ovonic threshold switch (OTS)-based nano-oscillators (OTSNOs).

As there is an increasing need for an efficient solver of combinatorial optimization problems, much interest is paid to the Ising machine, which is a novel physics-driven computing system composed of coupled oscillators mimicking the dynamics of the system of coupled electronic spins. In this work, we propose an energy-efficient nano-oscillator, called OTSNO, which is composed of an Ovonic Threshold Switch (OTS) and an electrical resistor. We demonstrate that the OTSNO shows the synchronization behavior, an essential property for the realization of an Ising machine. Furthermore, we have discovered that the capacitive coupling is advantageous over the resistive coupling for the hardware implementation of an Ising solver by providing a larger margin of the variations of components. Finally, we implement an Ising machine composed of capacitively-coupled OTSNOs to demonstrate that the solution to a 14-node MaxCut problem can be obtained in 40 µs while consuming no more than 2.3 µJ of energy. Compared to a previous hardware implementation of the phase-transition nano-oscillator (PTNO)-based Ising machine, the OTSNO-based Ising machine in this work shows the performance of the increased speed by more than one order while consuming less energy by about an order.

Hybrid CMOS-Memristor synapse circuits for implementing Ca ion-based plasticity model.

Neuromorphic computing research is being actively pursued to address the challenges posed by the need for energy-efficient processing of big data. One of the promising approaches to tackle the challenges is the hardware implementation of spiking neural networks (SNNs) with bio-plausible learning rules. Numerous research works have been done to implement the SNN hardware with different synaptic plasticity rules to emulate human brain operations. While a standard spike-timing-dependent-plasticity (STDP) rule is emulated in many SNN hardware, the various STDP rules found in the biological brain have rarely been implemented in hardware. This study proposes a CMOS-memristor hybrid synapse circuit for the hardware implementation of a Ca ion-based plasticity model to emulate the various STDP curves. The memristor was adopted as a memory device in the CMOS synapse circuit because memristors have been identified as promising candidates for analog non-volatile memory devices in terms of energy efficiency and scalability. The circuit design was divided into four sub-blocks based on biological behavior, exploiting the non-volatile and analog state properties of memristors. The circuit was designed to vary weights using an H-bridge circuit structure and PWM modulation. The various STDP curves have been emulated in one CMOS-memristor hybrid circuit, and furthermore a simple neural network operation was demonstrated for associative learning such as Pavlovian conditioning. The proposed circuit is expected to facilitate large-scale operations for neuromorphic computing through its scale-up.

Highly Luminescent Shell-Less Indium Phosphide Quantum Dots Enabled by Atomistically Tailored Surface States.

Contrary to the prevailing notion that shell structures arise from the intricate chemistry and surface defects of InP quantum dots (QDs), an innovative strategy that remarkably enhances the luminescence efficiency of core-only InP QDs to over 90% is introduced. This paradigm shift is achieved through the concurrent utilization of group 2 and 3 metal-derived ligands, providing an effective remedy for surface defects and facilitating charge recombination. Specifically, a combination of Zn carboxylate and Ga chloride is employed to address the undercoordination issues associated with In and P atoms, leading to the alleviation of in-gap trap states. The intricate interplay and proportional ratio between Ga- and Zn-containing ligands play pivotal roles in attaining record-high luminescence efficiency in core-only InP QDs, as successfully demonstrated across various sizes and color emissions. Moreover, the fabrication of electroluminescent devices relying solely on InP core emission opens a new direction in optoelectronics, demonstrating the potential of the approach not only in optoelectronic applications but also in catalysis or energy conversion by charge transfer.

Extrinsic hydrophobicity-controlled silver nanoparticles as efficient and stable catalysts for CO2 electrolysis.

To realize economically feasible electrochemical CO2 conversion, achieving a high partial current density for value-added products is particularly vital. However, acceleration of the hydrogen evolution reaction due to cathode flooding in a high-current-density region makes this challenging. Herein, we find that partially ligand-derived Ag nanoparticles (Ag-NPs) could prevent electrolyte flooding while maintaining catalytic activity for CO2 electroreduction. This results in a high Faradaic efficiency for CO (>90%) and high partial current density (298.39 mA cm‒2), even under harsh stability test conditions (3.4 V). The suppressed splitting/detachment of Ag particles, due to the lipid ligand, enhance the uniform hydrophobicity retention of the Ag-NP electrode at high cathodic overpotentials and prevent flooding and current fluctuations. The mass transfer of gaseous CO2 is maintained in the catalytic region of several hundred nanometers, with the smooth formation of a triple phase boundary, which facilitate the occurrence of CO2RR instead of HER. We analyze catalyst degradation and cathode flooding during CO2 electrolysis through identical-location transmission electron microscopy and operando synchrotron-based X-ray computed tomography. This study develops an efficient strategy for designing active and durable electrocatalysts for CO2 electrolysis.

Design of mid-infrared filter array based on plasmonic metal nanodiscs array and its application to on-chip spectrometer.

Mid-infrared wavelengths are called the molecular fingerprint region because it contains the fundamental vibrational modes inherent to the substances of interest. Since the mid-infrared spectrum can provide non-destructive identification and quantitative analysis of unknown substances, miniaturized mid-infrared spectrometers for on-site diagnosis have attained great concern. Filter-array based on-chip spectrometer has been regarded as a promising alternative. In this study, we explore a way of applying a pillar-type plasmonic nanodiscs array, which is advantageous not only for excellent tunability of resonance wavelength but also for 2-dimensional integration through a single layer process, to the multispectral filter array for the on-chip spectrometer. We theoretically and experimentally investigated the optical properties of multi-periodic triangular lattices of metal nanodiscs array that act as stopband filters in the mid-infrared region. Soft-mold reverse nanoimprint lithography with a subsequent lift-off process was employed to fabricate the multispectral filter array and its filter function was successfully extracted using a Fourier transform infrared microscope. With the measured filter function, we tested the feasibility of target spectrum reconstruction using a Tikhonov regularization method for an ill-posed linear problem and evaluated its applicability to the infrared spectroscopic sensor that monitors an oil condition. These results not only verify that the multispectral filter array composed of stopband filters based on the metal nanodiscs array when combined with the spectrum reconstruction technique, has great potential for use to a miniaturized mid-infrared on-chip spectrometer, but also provide effective guidance for the filter design.

Novel nano-plasmonic sensing platform based on vertical conductive bridge.

A novel nano-plasmonic sensing platform based on vertical conductive bridge was suggested as an alternative geometry for taking full advantages of unique properties of conductive junction while substantially alleviating burdens in lithographic process. The effects of various geometrical parameters on the plasmonic properties were systematically investigated. Theoretical simulation on this structure demonstrates that the presence of vertical conductive bridge with smaller diameter sandwiched between two adjacent thin nanodiscs excites a bridged mode very similar to the charge transfer plasmon and exhibits a remarkable enhancement in the extinction efficiency and the sensitivity when the electric field of incident light is parallel to the conductive bridge. Furthermore, for the electric field perpendicular to the bridge, another interesting feature is observed that two magnetic resonance modes are excited symmetrically through open-gaps on both sides of the bridge together with strongly enhanced electric field intensity, which provides a very favorable environment as a surface enhanced Raman scattering substrate for fluid analysis. These results verify a great potential and versatility of our approach for use as a nanoplasmonic sensing platform. In addition, we demonstrated the feasibility of fabrication process of vertical conductive bridge and high tunability in controlling the bridge width.

Realization of an Artificial Visual Nervous System using an Integrated Optoelectronic Device Array.

Human behavior (e.g., the response to any incoming information) has very complex forms and is based on the response to consecutive external stimuli entering varied sensory receptors. Sensory adaptation is an elementary form of the sensory nervous system known to filter out irrelevant information for efficient information transfer from consecutive stimuli. As bioinspired neuromorphic electronic system is developed, the functionality of organs shall be emulated at a higher level than the cell. Because it is important for electronic devices to possess sensory adaptation in spiking neural networks, the authors demonstrate a dynamic, real-time, photoadaptation process to optical irradiation when repeated light stimuli are presented to the artificial photoreceptor. The filtered electrical signal generated by the light and the adapting signal produces a specific range of postsynaptic states through the neurotransistor, demonstrating changes in the response according to the environment, as normally perceived by the human brain. This successfully demonstrates plausible biological sensory adaptation. Further, the ability of this circuit design to accommodate changes in the intensity of bright or dark light by adjusting the sensitivity of the artificial photoreceptor is demonstrated. Thus, the proposed artificial photoreceptor circuits have the potential to advance neuromorphic device technology by providing sensory adaptation capabilities.

Controllable modulation of precursor reactivity using chemical additives for systematic synthesis of high-quality quantum dots.

The optical and electronic performance of quantum dots (QDs) are affected by their size distribution and structural quality. Although the synthetic strategies for size control are well established and widely applicable to various QD systems, the structural characteristics of QDs, such as morphology and crystallinity, are tuned mostly by trial and error in a material-specific manner. Here, we show that reaction temperature and precursor reactivity, the two parameters governing the surface-reaction kinetics during growth, govern the structural quality of QDs. For conventional precursors, their reactivity is determined by their chemical structure. Therefore, a variation of precursor reactivity requires the synthesis of different precursor molecules. As a result, existing precursor selections often have significant gaps in reactivity or require synthesis of precursor libraries comprising a large number of variants. We designed a sulfur precursor employing a boron-sulfur bond, which enables controllable modulation of their reactivity using commercially available Lewis bases. This precursor chemistry allows systematic optimization of the reaction temperature and precursor reactivity using a single precursor and grows high-quality QDs from cores of various sizes and materials. This work provides critical insights into the nanoparticle growth process and precursor designs, enabling the systematic preparation of high-quality QD of any sizes and materials.

Optimization of tunable guided-mode resonance filter based on refractive index modulation of graphene.

To fabricate a tunable optical filter with a fast response in the near infrared region, a tunable guided-mode resonance (GMR) filter using graphene was proposed and its performance was optimized. In this study, a rigorous coupled wave analysis method was employed to systematically investigate the effects of geometrical configuration of graphene-integrated GMR filters and the optical properties of constituent materials including graphene on their spectral response in terms of tunability and extinction ratio. It was found that as the graphene is located close to the waveguide and the evanescent-field strength at the interface increases, the GMR filter exhibits better tunability. The bandwidth of the filter could be drastically reduced by adopting a low-index contrast grating layer, so that the extinction ratio of an optical signal could be greatly improved from 0.91 dB to 27.99 dB as the index contrast decreased from 0.99 to 0.47, respectively. Furthermore, new practical device designs, that is easy to fabricate and effectively implement the electric-field doping of graphene at low gate voltage, were also suggested and theoretically validated. These results demonstrate not only the excellent potential of a graphene-based tunable GMR filter but also provide practical design guidelines for optimizing the device performance.

Next-generation in vivo optical imaging with short-wave infrared quantum dots.

For in vivo imaging, the short-wavelength infrared region (SWIR; 1000-2000 nm) provides several advantages over the visible and near-infrared regions: general lack of autofluorescence, low light absorption by blood and tissue, and reduced scattering. However, the lack of versatile and functional SWIR emitters has prevented the general adoption of SWIR imaging by the biomedical research community. Here, we introduce a class of high-quality SWIR-emissive indium-arsenide-based quantum dots (QDs) that are readily modifiable for various functional imaging applications, and that exhibit narrow and size-tunable emission and a dramatically higher emission quantum yield than previously described SWIR probes. To demonstrate the unprecedented combination of deep penetration, high spatial resolution, multicolor imaging and fast-acquisition-speed afforded by the SWIR QDs, we quantified, in mice, the metabolic turnover rates of lipoproteins in several organs simultaneously and in real time as well as heartbeat and breathing rates in awake and unrestrained animals, and generated detailed three-dimensional quantitative flow maps of the mouse brain vasculature.

Photovoltaic Performance of PbS Quantum Dots Treated with Metal Salts.

Recent advances in quantum dot surface passivation have led to a rapid development of high-efficiency solar cells. Another critical element for achieving efficient power conversion is the charge neutrality of quantum dots, as charge imbalances induce electronic states inside the energy gap. Here we investigate how the simultaneous introduction of metal cations and halide anions modifies the charge balance and enhances the solar cell efficiency. The addition of metal salts between QD deposition and ligand exchange with 1,3-BDT results in an increase in the short-circuit current and fill factor, accompanied by a distinct reduction in a crossover between light and dark current density-voltage characteristics.

A path to practical Solar Pumped Lasers via Radiative Energy Transfer.

The optical conversion of incoherent solar radiation into a bright, coherent laser beam enables the application of nonlinear optics to solar energy conversion and storage. Here, we present an architecture for solar pumped lasers that uses a luminescent solar concentrator to decouple the conventional trade-off between solar absorption efficiency and the mode volume of the optical gain material. We report a 750-µm-thick Nd(3+)-doped YAG planar waveguide sensitized by a luminescent CdSe/CdZnS (core/shell) colloidal nanocrystal, yielding a peak cascade energy transfer of 14%, a broad spectral response in the visible portion of the solar spectrum, and an equivalent quasi-CW solar lasing threshold of 23 W-cm(-2), or approximately 230 suns. The efficient coupling of incoherent, spectrally broad sunlight in small gain volumes should allow the generation of coherent laser light from intensities of less than 100 suns.

Identifying and Eliminating Emissive Sub-bandgap States in Thin Films of PbS Nanocrystals.

Chemical oxidation of under-charged Pb atoms reduces the density of trap states by a factor of 40 in films of colloidal PbS quantum dots for devices. These emissive sub-bandgap states are a byproduct of several standard ligand-exchange procedures. X-ray photoelectron spectro-scopy measurements and density function theory simulations demonstrate that they are associated with under-charged Pb.

High-performance shortwave-infrared light-emitting devices using core-shell (PbS-CdS) colloidal quantum dots.

Core-shell PbS-CdS quantum dots enhance the peak external quantum efficiency of shortwave-infrared light-emitting devices by up to 50-100-fold (compared with core-only PbS devices). This is more than double the efficiency of previous quantum-dot light-emitting devices operating at wavelengths beyond 1 µm, and results from the passivation of the PbS cores by the CdS shells against in situ photoluminescence quenching.