Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems.

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

In-memory photonic dot-product engine with electrically programmable weight banks.

Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic-electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic-electronic dot-product engine, one that decouples electronic programming of phase-change materials (PCMs) and photonic computation. Specifically, we develop non-volatile electronically reprogrammable PCM memory cells with a record-high 4-bit weight encoding, the lowest energy consumption per unit modulation depth (1.7 nJ/dB) for Erase operation (crystallization), and a high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us to perform parallel multiplications for image processing with a superior contrast-to-noise ratio (≥87.36) that leads to an enhanced computing accuracy (standard deviation σ ≤ 0.007). An in-memory hybrid computing system is developed in hardware for convolutional processing for recognizing images from the MNIST database with inferencing accuracies of 86% and 87%.

Scalable High-Precision Trimming of Photonic Resonances by Polymer Exposure to Energetic Beams.

Integrated photonic circuits (PICs) have seen an explosion in interest, through to commercialization in the past decade. Most PICs rely on sharp resonances to modulate, steer, and multiplex signals. However, the spectral characteristics of high-quality resonances are highly sensitive to small variations in fabrication and material constants, which limits their applicability. Active tuning mechanisms are commonly employed to account for such deviations, consuming energy and occupying valuable chip real estate. Readily employable, accurate, and highly scalable mechanisms to tailor the modal properties of photonic integrated circuits are urgently required. Here, we present an elegant and powerful solution to achieve this in a scalable manner during the semiconductor fabrication process using existing lithography tools: by exploiting the volume shrinkage exhibited by certain polymers to permanently modulate the waveguide's effective index. This technique enables broadband and lossless tuning with immediate applicability in wide-ranging applications in optical computing, telecommunications, and free-space optics.

Midinfrared Spectroscopic Analysis of Aqueous Mixtures Using Artificial-Intelligence-Enhanced Metamaterial Waveguide Sensing Platform.

As miniaturized solutions, mid-infrared (MIR) waveguide sensors are promising for label-free compositional detection of mixtures leveraging plentiful absorption fingerprints. However, the quantitative analysis of liquid mixtures is still challenging using MIR waveguide sensors, as the absorption spectrum overlaps for multiple organic components accompanied by strong water absorption background. Here, we present an artificial-intelligence-enhanced metamaterial waveguide sensing platform (AIMWSP) for aqueous mixture analysis in the MIR. With the sensitivity-improved metamaterial waveguide and assistance of machine learning, the MIR absorption spectra of a ternary mixture in water can be successfully distinguished and decomposed to single-component spectra for predicting concentration. A classification accuracy of 98.88% for 64 mixing ratios and 92.86% for four concentrations below the limit of detection (972 ppm, based on 3σ) with steps of 300 ppm are realized. Besides, the mixture concentration prediction with root-mean-squared error varying from 0.107 vol % to 1.436 vol % is also achieved. Our work indicates the potential of further extending this sensing platform to MIR spectrometer-on-chip aiming for the data analytics of multiple organic components in aqueous environments.

Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy.

Infrared (IR) plasmonic nanoantennas (PNAs) are powerful tools to identify molecules by the IR fingerprint absorption from plasmon-molecules interaction. However, the sensitivity and bandwidth of PNAs are limited by the small overlap between molecules and sensing hotspots and the sharp plasmonic resonance peaks. In addition to intuitive methods like enhancement of electric field of PNAs and enrichment of molecules on PNAs surfaces, we propose a loss engineering method to optimize damping rate by reducing radiative loss using hook nanoantennas (HNAs). Furthermore, with the spectral multiplexing of the HNAs from gradient dimension, the wavelength-multiplexed HNAs (WMHNAs) serve as ultrasensitive vibrational probes in a continuous ultra-broadband region (wavelengths from 6 µm to 9 µm). Leveraging the multi-dimensional features captured by WMHNA, we develop a machine learning method to extract complementary physical and chemical information from molecules. The proof-of-concept demonstration of molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) shows 100% identification accuracy from the microfluidic integrated WMHNAs. Our work brings another degree of freedom to optimize PNAs towards small-volume, real-time, label-free molecular recognition from various species in low concentrations for chemical and biological diagnostics.

Larger-Than-Unity External Optical Field Confinement Enabled by Metamaterial-Assisted Comb Waveguide for Ultrasensitive Long-Wave Infrared Gas Spectroscopy.

Nanophotonic waveguides that implement long optical pathlengths on chips are promising to enable chip-scale gas sensors. Nevertheless, current absorption-based waveguide sensors suffer from weak interactions with analytes, limiting their adoptions in most demanding applications such as exhaled breath analysis and trace-gas monitoring. Here, we propose an all-dielectric metamaterial-assisted comb (ADMAC) waveguide to greatly boost the sensing capability. By leveraging large longitudinal electric field discontinuity at periodic high-index-contrast interfaces in the subwavelength grating metamaterial and its unique features in refractive index engineering, the ADMAC waveguide features strong field delocalization into the air, pushing the external optical field confinement factor up to 113% with low propagation loss. Our sensor operates in the important but underdeveloped long-wave infrared spectral region, where absorption fingerprints of plentiful chemical bonds are located. Acetone absorption spectroscopy is demonstrated using our sensor around 7.33 µm, showing a detection limit of 2.5 ppm with a waveguide length of only 10 mm.

Biometrics-protected optical communication enabled by deep learning-enhanced triboelectric/photonic synergistic interface.

Security is a prevailing concern in communication as conventional encryption methods are challenged by progressively powerful supercomputers. Here, we show that biometrics-protected optical communication can be constructed by synergizing triboelectric and nanophotonic technology. The synergy enables the loading of biometric information into the optical domain and the multiplexing of digital and biometric information at zero power consumption. The multiplexing process seals digital signals with a biometric envelope to avoid disrupting the original high-speed digital information and enhance the complexity of transmitted information. The system can perform demultiplexing, recover high-speed digital information, and implement deep learning to identify 15 users with around 95% accuracy, irrespective of biometric information data types (electrical, optical, or demultiplexed optical). Secure communication between users and the cloud is established after user identification for document exchange and smart home control. Through integrating triboelectric and photonics technology, our system provides a low-cost, easy-to-access, and ubiquitous solution for secure communication.

Suspended Silicon Waveguide with Sub-Wavelength Grating Cladding for Optical MEMS in Mid-Infrared.

Mid-infrared (MIR) photonics are generating considerable interest because of the potential applications in spectroscopic sensing, thermal imaging, and remote sensing. Silicon photonics is believed to be a promising solution to realize MIR photonic integrated circuits (PICs). The past decade has seen a huge growth in MIR PIC building blocks. However, there is still a need for the development of MIR reconfigurable photonics to enable powerful on-chip optical systems and new functionalities. In this paper, we present an MIR (3.7~4.1 µm wavelength range) MEMS reconfiguration approach using the suspended silicon waveguide platform on the silicon-on-insulator. With the sub-wavelength grating claddings, the photonic waveguide can be well integrated with the MEMS actuator, thus offering low-loss, energy-efficient, and effective reconfiguration. We present a simulation study on the waveguide design and depict the MEMS-integration approach. Moreover, we experimentally report the suspended waveguide with propagation loss (-2.9 dB/cm) and bending loss (-0.076 dB each). The suspended waveguide coupler is experimentally investigated. In addition, we validate the proposed optical MEMS approach using a reconfigurable ring resonator design. In conclusion, we experimentally demonstrate the proposed waveguide platform's capability for MIR MEMS-reconfigurable photonics, which empowers the MIR on-chip optical systems for various applications.

Heterogeneously Integrated Graphene/Silicon/Halide Waveguide Photodetectors toward Chip-Scale Zero-Bias Long-Wave Infrared Spectroscopic Sensing.

Mid-infrared absorption spectroscopy plays an important role in molecule identification and quantification for widespread applications. Integrated photonics provides opportunities to perform spectroscopic sensing on-chip for the minimization of device size, cost, and power consumption. The integration of waveguides and photodetectors is an indispensable step toward the realization of these on-chip sensing systems. It is desired to extend the operating wavelengths of these on-chip sensing systems to the long-wave infrared (LWIR) range to utilize more molecular absorption fingerprints. However, the development of LWIR waveguide-integrated photodetectors faces challenges from both waveguide platforms due to the bottom cladding material absorption and photodetection technologies due to the low LWIR photon energy. Here, we demonstrate LWIR waveguide-integrated photodetectors through heterogeneous integration of graphene photodetectors and Si waveguides on CaF2 substrates. A high-yield transfer printing method is developed for flexibly integrating the waveguide and substrate materials to solve the bottom cladding material absorption issue. The fabricated Si-on-CaF2 waveguides show low losses in the broad LWIR wavelength range of 6.3-7.1 µm. The graphene photodetector achieves a broadband responsivity of ∼8 mA/W in these low-photon-energy LWIR wavelengths under zero-bias operation with the help of waveguide integration and plasmonic enhancement. We further integrate the graphene photodetector with a Si-on-CaF2 folded waveguide and demonstrate on-chip absorption sensing using toluene as an example. These results reveal the potential of our technology for the realization of chip-scale, low-cost, and low-power-consumption LWIR spectroscopic sensing systems.

Electrochemically Exfoliated Platinum Dichalcogenide Atomic Layers for High-Performance Air-Stable Infrared Photodetectors.

Platinum dichalcogenide (PtX2), an emergent group-10 transition metal dichalcogenide (TMD) has shown great potential in infrared photonic and optoelectronic applications due to its layer-dependent electronic structure with potentially suitable bandgap. However, a scalable synthesis of PtSe2 and PtTe2 atomic layers with controlled thickness still represents a major challenge in this field because of the strong interlayer interactions. Herein, we develop a facile cathodic exfoliation approach for the synthesis of solution-processable high-quality PtSe2 and PtTe2 atomic layers for high-performance infrared (IR) photodetection. As-exfoliated PtSe2 and PtTe2 bilayer exhibit an excellent photoresponsivity of 72 and 1620 mA W-1 at zero gate voltage under a 1540 nm laser illumination, respectively, approximately several orders of magnitude higher than that of the majority of IR photodetectors based on graphene, TMDs, and black phosphorus. In addition, our PtSe2 and PtTe2 bilayer device also shows a decent specific detectivity of beyond 109 Jones with remarkable air-stability (>several months), outperforming the mechanically exfoliated counterparts under the laser illumination with a similar wavelength. Moreover, a high yield of PtSe2 and PtTe2 atomic layers dispersed in solution also allows for a facile fabrication of air-stable wafer-scale IR photodetector. This work demonstrates a new route for the synthesis of solution-processable layered materials with the narrow bandgap for the infrared optoelectronic applications.

Persistent Conjugated Backbone and Disordered Lamellar Packing Impart Polymers with Efficient n-Doping and High Conductivities.

Solution-processable highly conductive polymers are of great interest in emerging electronic applications. For p-doped polymers, conductivities as high a nearly 105 S cm-1 have been reported. In the case of n-doped polymers, they often fall well short of the high values noted above, which might be achievable, if much higher charge-carrier mobilities determined could be realized in combination with high charge-carrier densities. This is in part due to inefficient doping and dopant ions disturbing the ordering of polymers, limiting efficient charge transport and ultimately the achievable conductivities. Here, n-doped polymers that achieve a high conductivity of more than 90 S cm-1 by a simple solution-based co-deposition method are reported. Two conjugated polymers with rigid planar backbones, but with disordered crystalline structures, exhibit surprising structural tolerance to, and excellent miscibility with, commonly used n-dopants. These properties allow both high concentrations and high mobility of the charge carriers to be realized simultaneously in n-doped polymers, resulting in excellent electrical conductivity and thermoelectric performance.

A Stable Triplet-Ground-State Conjugated Diradical Based on a Diindenopyrazine Skeleton.

High-spin conjugated radicals have great potential in magnetic materials and organic spintronics. However, to obtain high-spin conjugated radicals is still quite challenging due to their poor stability. We report the successful synthesis and isolation of a stable triplet conjugated diradical, 10,12-diaryldiindeno[1,2-b:2',1'-e]pyrazine (m-DIP). With the m-xylylene analogue skeleton containing electron-deficient sp2 -nitrogen atoms, m-DIP displays significant aromatic character within its pyrazine ring and its spin density mainly delocalizes on the meta-pyrazine unit, making it a triplet ground state conjugated diradical. Our work provides an effective "spin density tuning" strategy for stable high-spin conjugated radicals.

Zero-bias mid-infrared graphene photodetectors with bulk photoresponse and calibration-free polarization detection.

Bulk photovoltaic effect (BPVE), featuring polarization-dependent uniform photoresponse at zero external bias, holds potential for exceeding the Shockley-Queisser limit in the efficiency of existing opto-electronic devices. However, the implementation of BPVE has been limited to the naturally existing materials with broken inversion symmetry, such as ferroelectrics, which suffer low efficiencies. Here, we propose metasurface-mediated graphene photodetectors with cascaded polarization-sensitive photoresponse under uniform illumination, mimicking an artificial BPVE. With the assistance of non-centrosymmetric metallic nanoantennas, the hot photocarriers in graphene gain a momentum upon their excitation and form a shift current which is nonlocal and directional. Thereafter, we demonstrate zero-bias uncooled mid-infrared photodetectors with three orders higher responsivity than conventional BPVE and a noise equivalent power of 0.12 nW Hz-1/2. Besides, we observe a vectorial photoresponse which allows us to detect the polarization angle of incident light with a single device. Our strategy opens up alternative possibilities for scalable, low-cost, multifunctional infrared photodetectors.

Multifunctional mid-infrared photonic switch using a MEMS-based tunable waveguide coupler.

We demonstrate a multifunctional photonic switch on silicon-on-insulator platform operating at the mid-infrared wavelength range (3.85-4.05 µm) using suspended waveguides with sub-wavelength cladding and a micro-electro-mechanical systems (MEMS) tunable waveguide coupler. Leveraging the flip-chip bonding technology, a top wafer acting as the electrode is assembled above the silicon-on-insular wafer to enable the electrostatic actuation. Experimental characterizations for the functions of the proposed device include (1) an optical attenuator with 25 dB depth using DC voltage actuation, (2) a 1×2 optical switch with response time of 8.9 µs and -3dB bandwidth up to 127 kHz using AC voltage actuation, and (3) an on-chip integrated light chopper with the comparable performance of a commercial rotating disc light chopper.

Demonstration of mid-infrared slow light one-dimensional photonic crystal ring resonator with high-order photonic bandgap.

Integrated mid-infrared sensing offers opportunities for the compact, selective, label-free and non-invasive detection of the absorption fingerprints of many chemical compounds, which is of great scientific and technological importance. To achieve high sensitivity, the key is to boost the interaction between light and analytes. So far, approaches like leveraging the slow light effect, increasing optical path length and enhancing the electric field confinement (f) in the analyte are envisaged. Here, we experimentally investigate a slow light one-dimensional photonic crystal ring resonator operating at high-order photonic bandgap (PBG) in mid-infrared range, which features both strong field confinement in analyte and slow light effect. And the optical path length can also be improved by the resoantor compared with waveguide structure. The characteristics of the first- and second-order bandgap edges are studied by changing the number of patterned periodical holes while keeping other parameters unchanged to confine the bands in the measurement range of our setup between 3.64 and 4.0 µm. Temperature sensitivity of different modes is also experimentally studied, which helps to understand the field confinement. Compared to the fundamental PBG edge modes, the second PBG edge modes show a higher field confinement in the analyte and a comparable group index, leading to larger light-matter interaction. Our work could be used for the design of ultra-sensitive integrated mid-infrared sensors, which have widespread applications including environment monitoring, biosensing and chemical analysis.

Nanometer-Scale Heterogeneous Interfacial Sapphire Wafer Bonding for Enabling Plasmonic-Enhanced Nanofluidic Mid-Infrared Spectroscopy.

As one of the most effective surface-enhanced infrared absorption (SEIRA) techniques, metal-insulator-metal structured metamaterial perfect absorbers possess an ultrahigh sensitivity and selectivity in molecular infrared fingerprint detection. However, most of the localized electromagnetic fields (i.e., hotspots) are confined in the dielectric layer, hindering the interaction between analytes and hotspots. By replacing the dielectric layer with the nanofluidic channel, we develop a sapphire (Al2O3)-based mid-infrared (MIR) hybrid nanofluidic-SEIRA (HN-SEIRA) platform for liquid sensors with the aid of a low-temperature interfacial heterogeneous sapphire wafer direct bonding technique. The robust atomic bonding interface is confirmed by transmission electron microscope observation. We also establish a design methodology for the HN-SEIRA sensor using coupled-mode theory to carry out the loss engineering and experimentally validate its feasibility through the accurate nanogap control. Thanks to the capillary force, liquid analytes can be driven into sensing hotspots without external actuation systems. Besides, we demonstrate an in situ real-time dynamic monitoring process for the acetone molecular diffusion in deionized water. A small concentration change of 0.29% is distinguished and an ultrahigh sensitivity (0.8364 pmol-1 %) is achieved. With the aid of IR fingerprint absorption, our HN-SEIRA platform brings the selectivity of liquid molecules with similar refractive indexes. It also resolves water absorption issues in traditional IR liquid sensors thanks to the sub-nm long light path. Considering the wide transparency window of Al2O3 in MIR (up to 5.2 µm), the HN-SEIRA platform covers more IR absorption range for liquid sensing compared to fused glass commonly used in micro/nanofluidics. Leveraging the aforementioned advantages, our work provides insights into developing a MIR real-time liquid sensing platform with intrinsic IR fingerprint selectivity, label-free ultrahigh sensitivity, and ultralow analyte volume, demonstrating a way toward quantitative molecule identification and dynamic analysis for the chemical and biological reaction processes.

Wearable Triboelectric/Aluminum Nitride Nano-Energy-Nano-System with Self-Sustainable Photonic Modulation and Continuous Force Sensing.

Wearable photonics offer a promising platform to complement the thriving complex wearable electronics system by providing high-speed data transmission channels and robust optical sensing paths. Regarding the realization of photonic computation and tunable (de)multiplexing functions based on system-level integration of abundant photonic modulators, it is challenging to reduce the overwhelming power consumption in traditional current-based silicon photonic modulators. This issue is addressed by integrating voltage-based aluminum nitride (AlN) modulator and textile triboelectric nanogenerator (T-TENG) on a wearable platform to form a nano-energy-nano-system (NENS). The T-TENG transduces the mechanical stimulations into electrical signals based on the coupling of triboelectrification and electrostatic induction. The self-generated high-voltage from the T-TENG is applied to the AlN modulator and boosts its modulation efficiency regardless of AlN's moderate Pockels effect. Complementarily, the AlN modulator's capacitive nature enables the open-circuit operation mode of T-TENG, providing the integrated NENS with continuous force sensing capability which is notably uninfluenced by operation speeds. Furthermore, a physical model is proposed to describe the coupled AlN modulator/T-TENG system. With the enhanced photonic modulation and the open-circuit operation mode enabled by synergies between the AlN modulator and the T-TENG, optical Morse code transmission and continuous human motion monitoring are demonstrated for practical wearable applications.

Wearable Triboelectric-Human-Machine Interface (THMI) Using Robust Nanophotonic Readout.

With the rapid advances in wearable electronics and photonics, self-sustainable wearable systems are desired to increase service life and reduce maintenance frequency. Triboelectric technology stands out as a promising versatile technology due to its flexibility, self-sustainability, broad material availability, low cost, and good scalability. Various triboelectric-human-machine interfaces (THMIs) have been developed including interactive gloves, eye blinking/body motion-triggered interfaces, voice/breath monitors, and self-induced wireless interfaces. Nonetheless, THMIs conventionally use electrical readout and produce pulse-like signals due to the transient charge flows, leading to unstable and lossy transfer of interaction information. To address this issue, we propose a strategy by equipping THMIs with robust nanophotonic aluminum nitride (AlN) modulators for readout. The electrically capacitive nature of AlN modulators enables THMIs to work in the open-circuit condition with negligible charge flows. Meanwhile, the interaction information is transduced from THMIs' voltage to AlN modulators' optical output via the electro-optic Pockels effect. Thanks to the negligible charge flow and the high-speed optical information carrier, stable, information-lossless, and real-time THMIs are achieved. Leveraging the design flexibility of THMIs and nanophotonic readout circuits, various linear sensitivities independent of force speeds are achieved in different interaction force ranges. Toward practical applications, we develop a smart glove to realize continuous real-time robotics control and virtual/augmented reality interaction. Our work demonstrates a generic approach for developing self-sustainable HMIs with stable, information-lossless, and real-time features for wearable systems.

Integration of MEMS IR detectors with MIR waveguides for sensing applications.

Waveguides have been utilized for label-free and miniaturized mid-infrared gas sensors that operate on the evanescent field absorption principle. For integrated systems, photodetectors based on the photocarrier generation principle are previously integrated with waveguides. However, due to the thermal excitation of carriers at room temperature, they suffer from large dark currents and noise in the long-wavelength region. In this paper, we introduce the integration of a MEMS-based broadband infrared thermopile sensor with mid-infrared waveguides via flip-chip bonding technology and demonstrate a proof-of-concept gas (N2O) sensor working at 3.9 µm. A photonic device with input and output grating couplers designed at 3.72 µm was fabricated on a silicon-on-insulator (SOI) platform and integrated with a bare thermopile chip on its output side via flip-chip bonding in order to realize an integrated photonic platform for a myriad range of sensing applications. A responsivity of 69 mV/W was measured at 3.72 µm for an 11 mm waveguide. A second device designed at 3.9 µm has a 1800 ppm resolution for N2O sensing.

Deep learning enables structured illumination microscopy with low light levels and enhanced speed.

Structured illumination microscopy (SIM) surpasses the optical diffraction limit and offers a two-fold enhancement in resolution over diffraction limited microscopy. However, it requires both intense illumination and multiple acquisitions to produce a single high-resolution image. Using deep learning to augment SIM, we obtain a five-fold reduction in the number of raw images required for super-resolution SIM, and generate images under extreme low light conditions (at least 100× fewer photons). We validate the performance of deep neural networks on different cellular structures and achieve multi-color, live-cell super-resolution imaging with greatly reduced photobleaching.