arXiv subjects

Volker J. Sorger

Publications and source records attributed to Volker J. Sorger.

At least 19 recordsLinked to original sources

arXiv2025

MoS2 Based 2D Material Photodetector Array with high Pixel Density

Arrays of photodetector-based pixel sensors are ubiquitous in modern devices, such as smart phone cameras, automobiles, drones, laptops etc. Two dimensional (2D) material-based photodetector arrays are a relevant candidate, especially for applications demanding planar formfactors. However, shortcomings in pixel density and prototyping without cross contamination limit technology adoption and impact. Also, while 2D material detectors offer high absorption, graphene's closed bandgap results in undesirably high dark currents. Here, we introduce the experimental demonstration of dense planar photodetector arrays. We demonstrate a micrometer narrow pitched 2D detector pixels and show this approach's repeatability by verifying performance of a 16-pixel array. Such dense and repeatable detector realization is enabled by a novel, selective, contamination free 2D material transfer system, that we report here in automated operation. The so realized photodetectors responsivity peaks at 0.8 A/W. Furthermore, we achieve uniform detector performance via bias voltage tuning calibration to maximize deployment. Finally, we demonstrate 2D arrayed photodetectors not only on a silicon chip platform but also demonstrate and very array performance on flexible polymer substrates. Densely arrayed, flat, bendable, and uniform performing photodetector pixels enable emerging technologies in the space where lightweight and reliable performance is required, such as for smart phones and emerging AR/VR markets, but smart gadgets, wearables, and for SWAP constrained aviation and space platforms.

physics.optics↗

arXiv2025

Near-energy-free Photonic Fourier Transformation for Convolution Operation Acceler

Convolutional operations are computationally intensive in artificial intelligence services, and their overhead in electronic hardware limits machine learning scaling. Here, we introduce a photonic joint transform correlator (pJTC) using a near-energy-free on-chip Fourier transformation to accelerate convolution operations. The pJTC reduces computational complexity for both convolution and cross-correlation from O(N4) to O(N2), where N2 is the input data size. Demonstrating functional Fourier transforms and convolution, this pJTC achieves 98.0% accuracy on an exemplary MNIST inference task. Furthermore, a wavelength-multiplexed pJTC architecture shows potential for high throughput and energy efficiency, reaching 305 TOPS/W and 40.2 TOPS/mm2, based on currently available foundry processes. An efficient, compact, and low-latency convolution accelerator promises to advance next-generation AI capabilities across edge demands, high-performance computing, and cloud services.

physics.optics↗

arXiv2023

From Talent Shortage to Workforce Excellence in the CHIPS Act Era: Harnessing Industry 4.0 Paradigms for a Sustainable Future in Domestic Chip Production

The CHIPS Act is driving the U.S. towards a self-sustainable future in domestic chip production. Decades of outsourced manufacturing, assembly, testing, and packaging has diminished the workforce ecosystem, imposing major limitations on semiconductor companies racing to build new fabrication sites as part of the CHIPS Act. In response, a systemic alliance between academic institutions, the industry, government, various consortiums, and organizations has emerged to establish a pipeline to educate and onboard the next generation of talent. Establishing a stable and continuous flow of talent requires significant time investments and comes with no guarantees, particularly factoring in the low workplace desirability in current fabrication houses for U.S workforce. This paper will explore the feasibility of two paradigms of Industry 4.0, automation and Augmented Reality(AR)/Virtual Reality(VR), to complement ongoing workforce development efforts and optimize workplace desirability by catalyzing core manufacturing processes and effectively enhancing the education, onboarding, and professional realms-all with promising capabilities amid the ongoing talent shortage and trajectory towards advanced packaging.

cs.CY↗

arXiv2023

2D material-based mode confinement engineering in electro-optic modulators

The ability to modulate light using 2-dimensional (2D) materials is fundamentally challenged by their small optical cross-section leading to miniscule modal confinements in diffraction-limited photonics despite intrinsically high electro-optic absorption modulation (EAM) potential given by their strong exciton binding energies. However the inherent polarization anisotropy in 2D-materials and device tradeoffs lead to additional requirements with respect to electric field directions and modal confinement. A detailed relationship between modal confinement factor and obtainable modulation strength including definitions on bounding limits are outstanding. Here we show that the modal confinement factor is a key parameter determining both the modulation strength and the modulator extinction ratio-to-insertion loss metric. We show that the modal confinement and hence the modulation strength of a single-layer modulated 2D material in a plasmonically confined mode is able to improve by more than 10x compared to diffraction-limited modes. Combined with the strong-index modulation of graphene the modulation strength can be more than 2-orders of magnitude higher compared to Silicon-based EAMs. Furthermore modal confinement was found to be synergistic with performance optimization via enhanced light-matter-interactions. These results show that there is room for scaling 2D material EAMs with respect to modal engineering towards realizing synergistic designs leading to high-performance modulators.

physics.optics↗

arXiv2023

Indium-Tin-Oxide for High-performance Electro-optic Modulation

Advances in opto-electronics are often led by discovery and development of materials featuring unique properties. Recently the material class of transparent conductive oxides (TCO) has attracted attention for active photonic devices on-chip. In particular Indium Tin Oxide (ITO) is found to have refractive index changes on the order of unity. This property makes it possible to achieve electro-optic modulation of sub-wavelength device scales, when thin ITO films are interfaced with optical light confinement techniques such as found in plasmonics; optical modes are compressed to nanometer scale to create strong light-matter-interactions. Here we review efforts towards utilizing this novel material for high-performance and ultra-compact modulation. While high performance metrics are achieved experimentally, there are open questions pertaining the permittivity modulation mechanism of ITO. Furthermore, we show that a footprint-saving waveguide inline cavity can enhance obtainable extinction-ratio to insertion-loss ratios by about one order of magnitude over non-cavity based version. Moreover, we offer a speed analysis that shows that the device is resistance limited, but not capacitance or drift-carrier limited. Interestingly, two bias options exist for ITO and we find that a side-connection enables devices that should in principle enable several hundred of GHz fast devices, using our routinely achievable ITO film resistivities. Finally, we offer a brief discuss about footprint savings of compact ITO modulators showing a 3-orders of magnitude smaller footprint over Silicon photonic MZI-based modulators.

physics.optics↗

arXiv2023

An electrically-driven Carbon nanotube-based plasmonic laser on Silicon

Photonic signal processing requires efficient on-chip light sources with higher modulation bandwidths. Todays conventional fastest semiconductor diode lasers exhibit modulation speeds only on the order of a few tens of GHz due to gain compression effects and parasitic electrical capacitances. Here we theoretically show an electrically-driven Carbon nanotube (CNT)-based laser utilizing strong light-matter-interaction via monolithic integration into Silicon photonic crystal nanobeam (PCNB) cavities. The laser is formed by single-walled CNTs inside a combo-cavity consisting of both a plasmonic metal-oxide-semiconductor hybrid mode embedded in the one dimensional PCNB cavity. The emission originates from interband recombinations of electrostatically-doped nanotubes depending on the tubes chirality towards matching the C-band. Our simulation results show that the laser operates at telecom frequencies resulting in a power output > 3 (100) uW and > 100 (1000)GHz modulation speed at 1x (10x) threshold. Such monolithic integration schemes provide an alternative promising approach for light source in future photonics integrated circuits.

physics.optics↗

arXiv2022

PhotoFourier: A Photonic Joint Transform Correlator-Based Neural Network Accelerator

The last few years have seen a lot of work to address the challenge of low-latency and high-throughput convolutional neural network inference. Integrated photonics has the potential to dramatically accelerate neural networks because of its low-latency nature. Combined with the concept of Joint Transform Correlator (JTC), the computationally expensive convolution functions can be computed instantaneously (time of flight of light) with almost no cost. This 'free' convolution computation provides the theoretical basis of the proposed PhotoFourier JTC-based CNN accelerator. PhotoFourier addresses a myriad of challenges posed by on-chip photonic computing in the Fourier domain including 1D lenses and high-cost optoelectronic conversions. The proposed PhotoFourier accelerator achieves more than 28X better energy-delay product compared to state-of-art photonic neural network accelerators.

cs.AR↗

arXiv2022

Integrated Photonic Tensor Processing Unit for a Matrix Multiply: a Review

The explosion of artificial intelligence and machine-learning algorithms, connected to the exponential growth of the exchanged data, is driving a search for novel application-specific hardware accelerators. Among the many, the photonics field appears to be in the perfect spotlight for this global data explosion, thanks to its almost infinite bandwidth capacity associated with limited energy consumption. In this review, we will overview the major advantages that photonics has over electronics for hardware accelerators, followed by a comparison between the major architectures implemented on Photonics Integrated Circuits (PIC) for both the linear and nonlinear parts of Neural Networks. By the end, we will highlight the main driving forces for the next generation of photonic accelerators, as well as the main limits that must be overcome.

cs.ET↗

arXiv2022

Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment

There has been growing interest in using photonic processors for performing neural network inference operations; however, these networks are currently trained using standard digital electronics. Here, we propose on-chip training of neural networks enabled by a CMOS-compatible silicon photonic architecture to harness the potential for massively parallel, efficient, and fast data operations. Our scheme employs the direct feedback alignment training algorithm, which trains neural networks using error feedback rather than error backpropagation, and can operate at speeds of trillions of multiply-accumulate (MAC) operations per second while consuming less than one picojoule per MAC operation. The photonic architecture exploits parallelized matrix-vector multiplications using arrays of microring resonators for processing multi-channel analog signals along single waveguide buses to calculate the gradient vector for each neural network layer in situ. We also experimentally demonstrate training deep neural networks with the MNIST dataset using on-chip MAC operation results. Our novel approach for efficient, ultra-fast neural network training showcases photonics as a promising platform for executing AI applications.

cs.LG↗

arXiv2022

Reconfigurable Application-Specific Photonic Integrated Circuit for solving Partial Differential Equations

Solving mathematical equations faster and more efficiently has been a Holy Grail for centuries for scientists and engineers across all disciplines. While electronic digital circuits have revolutionized equation solving in recent decades, it has become apparent that performance gains from brute-force approaches of compute-solvers are quickly saturating over time. Instead, paradigms that leverage the universes natural tendency to minimize a systems free energy, such as annealers or Ising Machines, are being sought after due to favorable complexity scaling. Here we introduce a programmable analog solver leveraging the mathematical formal equivalence between Maxwells equations and photonic circuitry. It features a mesh network of nanophotonic beams to find solutions to partial differential equations. As an example, we designed, fabricated, and demonstrated a novel application-specific photonic integrated circuit comprised of electro-optically reconfigurable nodes, and experimentally validated 90% accuracy with respect to a commercial solver. Finally, we tested this photonic integrated chip performance by simulating thermal diffusion on a spacecrafts heat shield during re-entry to a planets atmosphere. The programmable light-circuitry presented herein offers a facile route for solving complex problems and thus will have profound potential applications across many scientific and engineering fields.

physics.optics↗

arXiv2022

Self-Powered Broadband Photodetector Based on MoS2/Sb2Te3 Heterojunctions: A promising approach for highly sensitive detection

Topological insulators have shown great potential for future optoelectronic technology due to their extraordinary optical and electrical properties. Photodetectors, as one of the most widely used optoelectronic devices, are crucial for sensing, imaging, communication, and optical computing systems to convert optical signals to electrical signals. Here we experimentally show a novel combination of topological insulators (TIs) and transition metal chalcogenides (TMDs) based self-powered photodetectors with ultra-low dark current and high sensitivity. The photodetector formed by a MoS2/Sb2Te3 heterogeneous junction exhibits a low dark current of 2.4 pA at zero bias and 1.2 nA at 1V. It shows a high photoresponsivity of > 150 mA W-1 at zero bias and rectification of 3 times at an externally applied bias voltage of 1V. The excellent performance of the proposed photodetector with its innovative material combination of TMDs and TIs paves the way for the development of novel high-performance optoelectronic devices. The TIs/TMDs transfer used to form the heterojunction is simple to incorporate into on-chip waveguide systems, enabling future applications on highly integrated photonic circuits.

physics.optics↗

arXiv2022

High Throughput Multi-Channel Parallelized Diffraction Convolutional Neural Network Accelerator

Convolutional neural networks are paramount in image and signal processing including the relevant classification and training tasks alike and constitute for the majority of machine learning compute demand today. With convolution operations being computationally intensive, next generation hardware accelerators need to offer parallelization and algorithmic-hardware homomorphism. Fortunately, diffractive display optics is capable of million-channel parallel data processing at low latency, however, thus far only showed tens of Hertz slow single image and kernel capability, thereby significantly underdelivering from its performance potential. Here, we demonstrate an operation-parallelized high-throughput Fourier optic convolutional neural network accelerator. For the first time simultaneously processing of multiple kernels in Fourier domain enabled by optical diffraction has been achieved alongside with already conventional in the field input parallelism. Additionally, we show an about one hundred times system speed up over existing optical diffraction-based processors and this demonstration rivals performance of modern electronic solutions. Therefore, this system is capable of processing large-scale matrices about ten times faster than state of art electronic systems.

cs.LG↗

arXiv2022

Electrical Programmable Multi-Level Non-volatile Photonic Random-Access Memory

Photonic Random-Access Memories (P-RAM) are an essential component for the on-chip non-von Neumann photonic computing by eliminating optoelectronic conversion losses in data links. Emerging Phase Change Materials (PCMs) have been showed multilevel memory capability, but demonstrations still yield relatively high optical loss and require cumbersome WRITE-ERASE approaches increasing power consumption and system package challenges. Here we demonstrate a multi-state electrically-programmed low-loss non-volatile photonic memory based on a broadband transparent phase change material (Ge2Sb2Se5, GSSe) with ultra-low absorption in the amorphous state. A zero-static-power and electrically-programmed multi-bit P-RAM is demonstrated on a silicon-on-insulator platform, featuring efficient amplitude modulation up to 0.2 dB/μm and an ultra-low insertion loss of total 0.12 dB for a 4-bit memory showing a 100x improved signal to loss ratio compared to other phase-change-materials based photonic memories. We further optimize the positioning of dual micro-heaters validating performance tradeoffs. Experimentally we demonstrate a half-a million cyclability test showcasing the robust approach of this material and device. Low-loss photonic retention-of-state adds a key feature for photonic functional and programmable circuits impacting many applications including neural networks, LiDAR, and sensors for example.

physics.optics↗

arXiv2022

Nonlinear Optical Joint Transform Correlator for Low Latency Convolution Operations

Convolutions are one of the most relevant operations in artificial intelligence (AI) systems. High computational complexity scaling poses significant challenges, especially in fast-responding network-edge AI applications. Fortunately, the convolution theorem can be executed on-the-fly in the optical domain via a joint transform correlator (JTC) offering to fundamentally reduce the computational complexity. Nonetheless, the iterative two-step process of a classical JTC renders them unpractical. Here we introduce a novel implementation of an optical convolution-processor capable of near-zero latency by utilizing all-optical nonlinearity inside a JTC, thus minimizing electronic signal or conversion delay. Fundamentally we show how this nonlinear auto-correlator enables reducing the high $O(n^4)$ scaling complexity of processing two-dimensional data to only $O(n^2)$. Moreover, this optical JTC processes millions of channels in time-parallel, ideal for large-matrix machine learning tasks. Exemplary utilizing the nonlinear process of four-wave mixing, we show light processing performing a full convolution that is temporally limited only by geometric features of the lens and the nonlinear material's response time. We further discuss that the all-optical nonlinearity exhibits gain in excess of $>10^{3}$ when enhanced by slow-light effects such as epsilon-near-zero. Such novel implementation for a machine learning accelerator featuring low-latency and non-iterative massive data parallelism enabled by fundamental reduced complexity scaling bears significant promise for network-edge, and cloud AI systems.

physics.optics↗

arXiv2022

Self-Driven Highly Responsive PN Junction InSe Heterostructure Near-Infrared Light Detector

Photodetectors converting light signals into detectable photocurrents are ubiquitously in use today. To improve the compactness and performance of next-generation devices and systems, low dimensional materials provide rich physics to engineering the light matter interaction. Photodetectors based on two dimensional (2D) material van der Waals heterostructures have shown high responsivity and compact integration capability, mainly in the visible range due to their intrinsic bandgap. The spectral region of near-infrared (NIR) is technologically important featuring many data communication and sensing applications. While some initial NIR 2D material-based detectors have emerged, demonstrating doping junction based 2D material photodetectors with the capability to harness the charge separation photovoltaic effect are yet outstanding. Here, we demonstrate a 2D p-n van der Waals heterojunction photodetector constructed by vertically stacking p type and n type few layer indium selenide (InSe) 2D flakes. This heterojunction charge separation based photodetector shows a three fold enhancement in responsivity at near infrared spectral region (980 nm) as compared to a photoconductor detector based on p or n only doped regions, respectively. We show, that this junction device exhibits self-powered photodetection operation and hence enables few pA-low dark currents, which is about 4 orders of magnitude more efficient than state of the art foundry based devices.

physics.optics↗

arXiv2022

100 GHz Micrometer compact broadband Monolithic ITO Mach Zehnder Interferometer Modulator enabling 3500 times higher Packing Density

Electro-optic modulators provide a key function in optical transceivers and increasingly in photonic programmable Application Specific Integrated Circuits (ASICs) for machine learning and signal processing. However, both foundry ready silicon based modulators and conventional material based devices utilizing Lithium niobate fall short in simultaneously providing high chip packaging density and fast speed. Current driven ITO based modulators have the potential to achieve both enabled by efficient light matter interactions. Here, we introduce micrometer compact Mach Zehnder Interferometer (MZI) based modulators capable of exceeding 100 GHz switching rates. Integrating ITO thin films atop a photonic waveguide, spectrally broadband, and compact MZI phase shifter. Remarkably, this allows integrating more than 3500 of these modulators within the same chip area as only one single silicon MZI modulator. The modulator design introduced here features a holistic photonic, electronic, and RF-based optimization and includes an asymmetric MZI tuning step to optimize the Extinction Ratio (ER) to Insertion Loss (IL) and dielectric thickness sweep to balance the tradeoffs between ER and speed. Driven by CMOS compatible bias voltage levels, this device is the first to address next generation modulator demands for processors of the machine intelligence revolution, in addition to the edge and cloud computing demands as well as optical transceivers alike.

physics.optics↗

arXiv2021

Emerging Devices and Packaging Strategies for Electronic-Photonic AI Accelerators

The field of mimicking the structure of the brain on a chip is experiencing interest driven by the demand for machine intelligent applications. However, the power consumption and available performance of machine-learning (ML) accelerating hardware still leave much desire for improvement. In this letter, we share viewpoints, challenges, and prospects of electronic-photonic neural network (NN) accelerators. Combining electronics with photonics offers synergistic co-design strategies for high-performance AI Application-specific integrated circuits (ASICs) and systems. Taking advantages of photonic signal processing capabilities and combining them with electronic logic control and data storage is an emerging prospect. However, the optical component library leaves much to be desired and is challenged by the enormous size of photonic devices. Within this context, we will review the emerging electro-optic materials, functional devices, and systems packaging strategies that, when realized, provide significant performance gains and fuel the ongoing AI revolution, leading to a stand-alone photonics-inside AI ASIC 'black-box' for streamlined plug-and-play board integration in future AI processors.

physics.app-ph↗

arXiv2021

A Chirality-Based Quantum Leap

Chiral degrees of freedom occur in matter and in electromagnetic fields and constitute an area of research that is experiencing renewed interest driven by recent observations of the chiral-induced spin selectivity (CISS) effect in chiral molecules and engineered nanomaterials. The CISS effect underpins the fact that charge transport through nanoscopic chiral structures favors a particular electronic spin orientation, resulting in large room-temperature spin polarizations. Observations of the CISS effect suggest opportunities for spin control and for the design and fabrication of room-temperature quantum devices from the bottom up, with atomic-scale precision. Any technology that relies on optimal charge transport, including quantum devices for logic, sensing, and storage, may benefit from chiral quantum properties. These properties can be theoretically and experimentally investigated from a quantum information perspective, which is presently lacking. There are uncharted implications for the quantum sciences once chiral couplings can be engineered to control the storage, transduction, and manipulation of quantum information. This forward-looking perspective provides a survey of the experimental and theoretical fundamentals of chiral-influenced quantum effects, and presents a vision for their future roles in enabling room-temperature quantum technologies.

cond-mat.mes-hall↗