Here, we explores the forefront of optical dynamic real-time signal processing with the introduction of a Reconfigurable Complex Convolution Module (RCCM), leveraging Michelson Interferometric modulation and spatial light modulators (SLMs) for enhanced computational performance. By implementing a 4F-interferometer configuration, the RCCM facilitates simultaneous two-dimensional full complex convolutions, showcasing the ability for intricate amplitude and phase modulation within the Fourier domain. This approach significantly advances optical computing by demonstrating the RCCM’s capacity for parallel processing, superior speed, and energy efficiency. Our evaluation emphasizes the module’s impact on computational efficiency and throughput, highlighting its potential to revolutionize current computational paradigms by offering real-time reconfigurability, reduced energy consumption, and increased processing speeds. The integration of these optical computing techniques sets a new standard in addressing complex computational challenges, indicating a substantial leap towards high-speed, energy-efficient computing solutions.
In this paper, we present an improved version of an Application-Specific Photonic Integrated Chip for solving Partial Differential Equations (PDEs). This novel chip is designed to solve PDEs with specific reflecting boundary conditions, by means of a series of integrated mirrors at the edges.
In this work, we demonstrate a Sb2Te3/MoS2 heterostructure photodetector for visible range detection or sensing applications with ultra-low dark current and high sensitivity at zero external bias. The photoresponsivity can reach to 156 mA/W at zero bias and can be enhanced 3 times at 1V bias voltage.
Here we introduce a Fourier-Theorem based convolution processors in silicon photonics. The systems leverages an algorithmic homomorphism that utilizes the Fourier transformation provided by a lens along with high-speed optoelectronic signal modulation and read-out. We demonstrate convolution filtering for image processing, convolutional neural network classification tasks. An on-chip lens performs the convolution operation, whereas electro-optic modulators perform the weighting in the Fourier domain at high-speed, followed by detection at a detector array after a 2nd Fourier lens, all on a PIC. Using this accelerator, we demonstrate image filtering and machine learning inference tasks. Given the high SWAP, these accelerators are useful for network-edge AI for the coming Industry-4.0 era.
This study aimed to develop and implement a novel data encryption method that utilizes a hybrid processor Photonic Tensor Core and chaotic oscillators to generate an "infinite key" suitable for use with common encryption algorithms. To demonstrate its effectiveness, we built a prototype consisting of a hybrid processor simulator, chaotic oscillators, a key generator, an encryption/decryption tool, and a graphical user interface. We tested and inspected the tool using custom scripts and a graphical user interface, which allows two separate users to compare their respective results. In upcoming studies, we plan to expand the tool to accommodate multiple participants and develop a hardware prototype.
Here we introduce a free-space optical communication (FSOC) system that is capable of adjustment to alignment drift, varying atmospheric turbulent conditions of multiplexed spatial structured laser beams such as orbital angular momentum (OAM) beams. The detection system is based on heterogeneous convolutional neural network with first Fourier convolution neural network layer implemented in optics as a 4f system driven by kilohertz-fast reprogrammable high-resolution digital micromirror devices (DMDs). We utilize this optical-filtering-based convolutional neural network to realize the training and identification of two co-propagating OAM beams among 12 different multiplexed modes under simulated turbulent condition using modified von K´arm´an atmospheric model. The current implementation shows test accuracy of 95.04% (under weak turbulence) and 87.52% (under strong turbulence).
Phase-change materials offer a compelling platform for low power consumption active integrated optical circuits and meta optics, with their large optical index contrast (Δn, Δk) and nonvolatile phase transition1,2. Here, we demonstrate an electrically driven tunable meta lens in telecom range by exploring the full potential of a low absorption loss and high refractive index contrast PCM alloy, Sb2Se3, to realize non-volatile, reversible, fast focusing and defocusing meta lens in the 1550 telecom spectral range. With a fixed geometric design, the phase change material of Sb2Se3 switches the focusing length of a silicon photonic meta lens between two different values nonviolently. This unique functionality of the hybrid meta surface is attributed to the fact that the silicon’s refractive index is in the middle of the two convertible states in the optical phase change material. The transparency of Sb2Se3 in both states enables near phase-only meta surface structures. Our heterostructure architecture capitalizes over the integration of a robust resistive transparent microheater ITO (Indium Tin Oxide) decoupled from meta lens enabling good model to overlap with PCM meta pillars enables high transmission efficiency. The project be experimentally demonstrating an electrically reconfigurable phase-change meta lens capable of modulation an incident light beam into focusing of defocusing two different statures. This work represents a critical advance towards the development of integrable dynamic meta lens and their potential for beamforming applications.
Vertical-Cavity Surface-Emitting Lasers (VCSELs) have become the preferred option for energy-efficient, high-speed optical interconnects in data centers and supercomputers due to their cost-effectiveness and reliability. However, current VCSELs have limitations in modulation speeds, with a roll-off of 20 GHz. In this work, we propose a novel hexagonal transverse-coupled-cavity design that adiabatically couples through a central cavity. Through this design, we have successfully developed a prototype VCSEL with a 3-dB roll-off modulation bandwidth of 45 GHz, an improvement of five times that of a standard VCSEL on the same epiwafer. This design utilizes the Vernier effect to widen the laser's aperture, extending the dynamic roll-off point and increasing power output. With the increased modulation speed and output power, this new VCSEL design offers new possibilities for data communication, sensing, automotive, and photonic AI systems.
An experimental demonstration of a waveguide-integrated plasmonic slot photodetector based on MoTe2 with 30 GHz 3 dB roll-off bandwidth at telecom wavelength. To overcome the intrinsic low carrier mobility and weak light-matter interaction when applying two-dimensional material for optoelectronic devices, here we numerically and experimentally show a novel concept of the plasmonic slot structure to eliminate the transit time constant (τ=L^2/μV) from the device which is related to the material mobility. The nanometer-wide plasmonic slot offers a ‘squeezed’ mode that allows the 2d material can effectively absorb the light via band-to-band transitions with an overlap factor (Г) increasing more than 3 times compared to the bare waveguide structure. The ultra-narrow slot width reduces the carriers’ drift route to tens of nanometers and is only limited by the RC time constant. The MoTe2 serves as the semiconducting light-absorbing material with its layer-dependent bandgap that encompasses the standard O-band wavelength for communications (1,260 nm -1,360 nm). The device's static performance under 1 V bias voltage also shows a high photoresponsivity of 0.8 A/W at 1310 nm with a low dark current of 90 nA. Furthermore, we study the slot width-dependent frequency response and static response to validate our concept, which shows that both the frequency and static response are inversely proportional to the slot width. The concept is not restricted to materials and the platform. This may pave the way for developing high-performance optoelectronic devices with materials that have unique optical and electric properties but suffer from low mobilities.1–13 It has been shown that introducing plasmonic structures, cavities, resonators, or nanoparticles into waveguide-loaded systems can improve light-matter interactions.14–17 Still, one of their biggest challenges today is the frequency response of TMDCbased devices for telecommunications or information processing.18–30 For photodetectors using TMDCs as the lightabsorbing medium, this is particularly crucial.
Accurate simulation is a critical requirement for modern optical systems, but precise theoretical modeling can be challenging due to various factors such as misalignment, theoretical approximations, and instrument errors. This paper presents the black-box simulation method, which addresses these limitations by training the optical system model using the optical field outputs. This approach leads to more accurate simulations, making it possible to effectively train optical Neural Network systems for improved performance.
Decision-making through artificial neural networks with minimal latency is critical for numerous applications such as navigation, tracking, and real-time machine action systems. This requires machine learning hardware to process multidimensional data at high throughput. Unfortunately, handling convolution operations, the primary computational tool for data classification tasks, obeys challenging runtime complexity scaling laws. However, homomorphically implementing the convolution theorem in a Fourier optics display light processor can achieve a non-iterative O(1) runtime complexity for data inputs beyond 1,000 × 1,000 large matrices. Following this approach, here we demonstrate data streaming multi-kernel image batching using a Fourier Convolutional Neural Network (FCNN) accelerator. We show image batch processing of large-scale matrices as 2 million dot product multiplications performed by a digital light processing module in the Fourier domain. Furthermore, we further parallelize this optical FCNN system by exploiting multiple spatially parallel diffraction orders, achieving a 98x throughput improvement over state-of-the-art FCNN accelerators. A comprehensive discussion of the practical challenges associated with working at the edge of system capabilities highlights the problem of crosstalk and resolution scaling laws in the Fourier domain. Accelerating convolution by exploiting massive parallelism in display technology brings non-Van Neumann-based machine learning acceleration.
The rapid development of nanophotonic technologies has put forward higher requirements for optoelectronic devices, including ultra-small footprints, high-speed operation, high efficiency, and low power consumption. Optoelectronics based on emerging materials can provide the material framework that can keep pace with future technological demands. Here we will share our latest innovations and device demonstrations of using low-dimensional materials towards discovering high-performance photodetector and electro-optic modulator performances. We will share the concept of strainoptronics enabling to engineer a plurality of material properties (bandgap, workfunction, mobility) and show how a Transition-Metal Dichalcogenides (TMDC)-based efficient photodetector can be realized using MoS2 on a Silicon photonic platform. Furthermore, using scaling-length-theory, we show our roadmap and results of high gain-bandwidth product photodetectors using a metal slot atop a silicon photonic waveguide towards optimizing the carrier-lifetime to transit time ratio. These devices were enabled by a novel 3D-like 2D material transfer system, which also enabled us to demonstrate a 2D material PN junction photodetector operating at zero bias, thus leading to extremely low dark currents and hence very efficient noise-equivalent powers. Finally, we show our latest work on ITO-thin film electro-optic modulators with 40 GHz 3dB roll-off, requiring just 200 meV of the drive voltage. Further development of the modulator platform shows the potential of a 100 GHz fast MZI modulator with a footprint that is 1,000 more compact than standard Silicon photonics and 10,000 more compact compared to Lithium Niobite.
Optimizing the trade-off between high-speed and energy consumption in today's optoelectronic devices is becoming more complex. Achieving high-speed photonics with heterogeneous material integration requires millimeter-to-centimeter-scale footprints. Search for an electro-optic modulator with high speed, energy economy, and compactness continues. These results were achieved using a 2D material optical modulator integrated on a Silicon photonics platform. A vertical distributed-Bragg-reflector cavity boosts the electro-optic response while reducing the driving voltage by nearly 40 times while retaining modulation depth (5.2 dB/V). Low-power modulators provide high photonic chip density and performance (60 GHz), which is crucial for signal processing and analog and neuromorphic photonic computers. Next, I will share the implementation of the concept of the electrical Scale Length Theory asserts that for high-performance devices with a nominal factor, such as FETs, both the channel thickness and length scale with the nominal factor. When this concept is applied to photodetectors, the gain-bandwidth product (GBP), where is the distance between the electrodes and the route for collecting photo-generated carriers, is equal to the slot width. Here, we experimentally show that a waveguide-integrated, plasmonic MoTe2-based photodetector can efficiently detect light (0.8 A W-1) at 1310 nm at high speed (>30GHz) with 1 V bias voltage. Compact, efficient, and performance photodetectors and modulators are key for next-generation systems with applications in machine intelligence, network edge-processing, data-centers or cyber-security.
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