Articles tagged with Optical Computing
Researchers have successfully reprogrammed a tiny optical device using light, allowing for flexible and energy-efficient processing in optical computing. The device uses liquid crystals and a metasurface to control the properties of light, enabling the manipulation of optical components in real-time.
The new method uses a chemical additive to create a sacrificial molecular cushion on the crystal surface, allowing for smoother cutting and reducing defects. This technique slashes subsurface crystal defects to a depth of only 70 nanometers, promising to revolutionize semiconductor manufacturing.
Researchers create powerful optical device with layered semiconductor and metasurface, enhancing nonlinear frequency conversion and enabling efficient light mixing and transformation. The device has potential applications in telecommunications, quantum communication, and photonic quantum computing.
Optical convolution computation enables parallel light propagation and multiplexing for faster and more energy-efficient computing systems. The review organizes the field into two paradigms: definition-based and theorem-based, which leverage mathematical principles to implement convolution operations in the physical domain.
Researchers have developed a new photonic architecture that enables scalable spatiotemporal interleaving networks for high-density integrated photonic convolution. The SPIN (Spatiotemporal Photonic Interleaving Network) framework reduces waveguide complexity and increases programmability in wavelength-domain interleaving, enabling comp...
A new mathematical approach using optics helps computers solve larger, more complex optimization problems by reducing computational demands. The framework can be applied to various real-world challenges, including facility placement and data clustering, with potential benefits for a carbon-neutral future.
Researchers at MIT have developed a microscopic pixel-based tunable lens that controls incoming infrared light for more precise thermal imaging, chemical sensing, or pollution monitoring. The system enables compact, dynamic infrared cameras with potential applications in environmental protection, space research, and military technology.
Researchers at EPFL found that even without physical contact, nearby dielectrics can introduce additional energy loss in ultracoherent nanomechanical resonators. Trapped static electric charges cause energy to dissipate, leading to reduced mechanical coherence.
Researchers propose a new framework, BOC, inspired by biological nervous systems to build more adaptive intelligent systems. The framework integrates sensing, memory, and computation directly at the hardware level, reducing data movement and latency.
The new photonic architecture harnesses three fundamental degrees of freedom: wavelength, mode, and polarization, achieving 192 parallel computing channels. The chip supports large, reconfigurable convolution kernels up to 13x13, capturing global structural contours while preserving fine details.
Researchers developed a non-contact optical sensing strategy to detect ethanol molecules in air using light-field distortions and deep learning. The system employs a graphene-based Fresnel lens to focus light through interference, capturing minute changes in the focal spot formed by the lens.
ACM recognizes educators Yasmin Kafai and Mitchel Resnick for their contributions to creative computing and Scratch. Ed Felten receives the ACM Policy Award for his work on technology policy, while Jodi Tims is honored with the Outstanding Contribution to ACM Award for increasing women's participation in computing
Researchers at MIT have developed a technique to create nanoscale features in hydrogel materials, shrinking them down to 100 nanometers or smaller. This allows for the creation of photonic devices that can manipulate visible light, enabling optical computing and potential applications in high-speed imaging and information processing.
Researchers propose a novel THz metasurface-enabled platform for integrated sensing and imaging, overcoming limitations of slow sequential data acquisition. The system achieves 100% binary image reconstruction with nanosecond-scale accuracy, enabling real-time applications in security, semiconductor, and pharmaceutical sectors.
Engineers at Harvard create microcombs on photonic chips, enabling compact, programmable frequency combs for precision measurement and telecommunications applications. The breakthrough makes electro-optic microcombs more practical, energy efficient, and diverse.
A research team has pioneered a novel optical neural network that physically 'learns' through associative light exposure, eliminating the need for traditional computing algorithms. The system successfully recognized letters and demonstrated capability in handwritten digit recognition.
A new prototype device accelerates and reduces energy cost of AI computation by encoding data into light patterns, enabling faster and more efficient processing. This innovation aims to ease the energy bottleneck in AI technology, making it more sustainable and accessible for various applications.
A fiber neural network, proposed by Tsinghua University, processes information entirely within the optical domain, reducing latency and power consumption. The system achieved 100% classification accuracy in modulation format recognition and demonstrated robustness against noise, paving the way for real-world deployment.
McMaster and Pittsburgh researchers have developed a soft material that can perform a NAND logic operation using only three beams of visible light. The breakthrough paves the way for autonomous systems with computation capabilities without traditional electronics.
Optical computing harnesses light to accelerate feature extraction in AI applications. The new system, OFE2, achieves a 12.5 GHz operating rate and 250.5 ps latency, outperforming traditional digital processors.
The Stowers Institute has appointed its first AI Fellow, Sumner Magruder, to harness the potential of artificial intelligence in biological research. He will collaborate with researchers to design new algorithms and unlock insights from large datasets.
Researchers at Columbia University have identified the rules for creating perfect polaritons, which are hybrid quasiparticles combining light and matter. The guiding rules include large optical absorption, low disorder, and inherent exciton delocalization, enabling polaritons to preserve coherence despite strong interactions and disorder.
The POEM Technology Center in Denmark will produce advanced wafers for photonic chips, enabling the development of high-speed communication and optical data processing. The facility will also facilitate the production of quantum chips, a key component in large-scale quantum computing.
The new Harvard device can turn purely digital electronic inputs into analog optical signals at high speeds, addressing the bottleneck of computing and data interconnects. It has the potential to enable advances in microwave photonics and emerging optical computing approaches.
Researchers are combining machine learning algorithms with neuromorphic hardware to build brain-like devices that can learn from data and adapt in real-time. These devices have the potential to revolutionize industries such as manufacturing by enabling machines to sense their environment, adapt to new tasks, and make decisions without ...
This book provides a beginner-friendly resource on the impact and evolution of decentralized networks, highlighting their applications in healthcare, supply chains, agriculture, climate monitoring, and education. The authors emphasize sustainability, data security, and ethical tech adoption.
Researchers from Université Laval designed an ultra-fast and greener optical chip that can transfer massive amounts of data at speeds of 1,000 gigabits per second while reducing energy consumption. This innovation uses the phase of light to add a new dimension to the signal, reaching unprecedented performance levels.
Researchers from OIST develop new quantum AI method for image recognition based on boson sampling, achieving highly accurate results without complex training. The approach uses a linear optical network and preserves information, outperforming classical methods in various datasets.
Researchers at Aston University have developed a new class of ultralow loss optical microresonators that can be widely tunable and precisely controlled. The devices, formed at the intersection of two optical fibers, hold potential applications in communication, computing, sensing and more.
Researchers at U of A create a transistor that operates at speeds over 1,000 times faster than modern computer chips. The breakthrough uses quantum effects to manipulate electrons in graphene, enabling ultrafast processing for applications in space research, chemistry, and healthcare.
Researchers have developed an on-chip twisted moiré photonic crystal sensor that can simultaneously measure wavelength, polarization, and perform hyperspectral imaging. The device uses MEMS technology to control the twist and distance between layers in real time.
Researchers at Harvard created a new type of interferometer that can modulate aspects of light in one compact package, enabling precise control over light's frequency and intensity. This breakthrough has the potential to be used in advanced nanophotonic sensors or on-chip quantum computing.
Researchers developed new photon avalanching nanoparticles that exhibit high nonlinearities, overcoming challenges in realizing intrinsic optical bistability at the nanoscale. The breakthrough paves the way for fabricating optical memory and transistors on a nanometer scale comparable to current microelectronics.
Researchers developed a groundbreaking photonic platform to overcome limitations in in-memory computing, enabling faster calculations and greater efficiency. The innovative magneto-optical memories consume about one-tenth the power of traditional electronics and can be rewritten billions of times.
Scientists successfully prepared six mechanical oscillators in a collective state, observing phenomena that emerge when oscillators act as a group. The research demonstrates experimental confirmation of theories about collective quantum behavior, opening new possibilities for quantum sensing and generation of multi-partite entanglement.
Researchers developed a large-scale optical programmable logic array that can execute complex models like Conway's Game of Life, marking a significant advancement in optical computing. The array uses parallel spectrum modulation to achieve an 8-input system, significantly expanding the capabilities of optical logic operations.
Researchers have developed a reconfigurable three-dimensional integrated photonic processor specifically designed to tackle the subset sum problem, a classic NP-complete challenge. The processor operates by allowing photons in a light beam to explore all possible paths simultaneously, providing answers in parallel and demonstrating hig...
Researchers from Pitt, UC Santa Barbara, University of Cagliari, and Institute of Science Tokyo have developed a new method for photonic in-memory computing that combines non-volatility, multibit storage, high switching speed, low switching energy, and high endurance in a single platform.
Researchers at KTH Royal Institute of Technology have developed a novel 3D printing method to fabricate glass micro-supercapacitors with enhanced performance. The approach utilizes ultrashort laser pulses to create electrodes with increased surface area and rapid ion transport, leading to improved energy storage capabilities.
Researchers developed a novel optical computation architecture called diffraction casting, which leverages spatial parallelism of light to perform computations. This method overcomes limitations of previous techniques by using wave optics, enabling scalable and parallel logic operations with high flexibility and integration capability.
Researchers at the University of Tokyo introduce a new optical computing scheme called diffraction casting, which improves upon existing methods. The system uses light waves to perform logic operations and has shown promise in running complex calculations, including those used in machine learning.
Researchers developed a structure called multiplexed neuron sets to reduce crosstalk in optical neural networks. The new backpropagation training algorithm achieved comparable performance while improving energy efficiency by a factor of 10.
A research team has successfully created a new dimension in photonic machine learning by incorporating sound waves, enabling the creation of reconfigurable neuromorphic building blocks. This innovation has the potential to revolutionize computing tasks by providing high-speed and large-capacity solutions.
Researchers extend spatially incoherent diffractive networks to perform complex-valued linear transformations with negligible error, opening up new applications in fields like autonomous vehicles. This breakthrough enables the encryption and decryption of complex-valued images using spatially incoherent diffractive networks.
Researchers at Paul Scherrer Institute created solid-state qubits from rare-earth ions in a crystal, showing that long coherences can exist in cluttered environments. The approach uses strongly interacting pairs of ions to form qubits, which are shielded from the environment and protected from decoherence.
Researchers developed three diffractive deep neural networks using orbital angular momentum to recognize objects in images, achieving accuracy comparable to wavelength and polarization-based models. The technology has potential for real-time processing applications like image recognition and data-intensive tasks.
The development of a new photonic technique enables the precise control of photonic angular momentum, allowing for the efficient recognition and real-time control of total angular momentum modes. The technique, which involves the symmetrical cascading of two units, has been experimentally demonstrated to recognize up to 42 individual T...
Researchers designed a simplified Mach-Zehnder interferometer mesh for real-valued matrix-vector multiplication, reducing hardware requirements and energy consumption. The new mesh detects incoherent light and is scalable, making it suitable for large-scale optical neural networks.
Researchers have discovered a way to utilize nonlinear scattering media for optical computing and machine learning. They created a novel theoretical framework involving third-order tensors, which can represent the complex relationships between input and output signals. This breakthrough has potential applications in real-world settings...
A breakthrough in photonic memory has been achieved, enabling fast volatile modulation and nonvolatile weight storage for rapid training of optical neural networks. The 5-bit photonic memory utilizes a low-loss PCM antimonite to achieve rapid response times and energy-efficient processing.
Researchers developed a new photonic blockchain called LightHash that uses a silicon photonics chip to reduce energy consumption in cryptocurrency mining. The approach could enable low-energy optical computing, reducing data centers' energy consumption and paving the way for more eco-friendly cryptocurrencies.
Scientists create a simple approach to fabricating highly precise 3D aperiodic photonic volume elements (APVEs) for various applications. The method uses direct laser writing to arrange voxels of specific refractive indices in glass, enabling the precise control of light flow and achieving record-high diffraction efficiency.
Researchers achieved optical switching of a light signal at attosecond speeds, exceeding data transfer speeds by 1 million times. This breakthrough enables the development of ultrafast optical electronics and could increase data processing speed in long-distance communications.
Diffractive deep neural networks enable objects to be classified through unknown random diffusers, offering high speed, parallelism and low power consumption. The single-pixel broadband diffractive network achieved a blind testing accuracy of 87.74% in recognizing handwritten digits.
Researchers have developed a diffractive optical processor that can compute hundreds of transformations in parallel using wavelength multiplexing. The processor, which is powered by light instead of electricity, can execute multiple complex functions simultaneously at the speed of light.
Researchers at Aalto University have developed a new optical computing approach that uses circularly polarized light to operate logic gates, resulting in ultrafast processing speeds. The technology operates about one million times faster than existing technologies and can be integrated into a single device.
Researchers at Harvard John A. Paulson School of Engineering and Applied Sciences have developed an integrated electro-optic modulator that can efficiently change the frequency and bandwidth of single photons on a chip. This device could be used for more advanced quantum computing and quantum networks.
Harvard scientists create a high-performance on-chip femtosecond pulse source using a time lens, enabling broadband, high-intensity pulse sources. The device is highly tunable, integrated onto a small chip and requires reduced power compared to traditional table-top systems.
A team from Harvard John A. Paulson School of Engineering and Applied Sciences has developed an electro-optic frequency comb that is 100-times more efficient and has more than twice the bandwidth of previous state-of-the-art versions.
Researchers have designed an energy-efficient silicon-based non-volatile switch that manipulates light to control information flow in data centers. This technology reduces energy needs by 70-fold compared to traditional switches, making data centers more environmentally friendly.