Photonic Computing Innovations

Using light as a neural network, as this viral video depicts, is actually closer than you think. In 5-10yrs, we could have matrix multiplications in constant time O(1) with 95% less energy. This is the next era of Moore's Law. Let's talk about Silicon Photonics... The core concept: Replace electrical signals with photons. While current processors push electrons through metal pathways, photonic systems use light beams, operating at fundamentally higher speeds (electronic signals in copper are 3x slower) with minimal heat generation. It's way faster. While traditional chips operate at 3-5 GHz, photonic devices can achieve >100 GHz switching speeds. Current interconnects max out at ~100 Gb/s. Photonic links have demonstrated 2+ Tb/s on a single channel. A single optical path can carry 64+ signals. It's way more energy efficient. Current chip-to-chip communication costs ~1-10pJ/bit. Photonic interconnects demonstrate 0.01-0.1pJ/bit. For data centers processing exabytes, this 200x improvement means the difference between megawatt and kilowatt power requirements. The AI acceleration potential is revolutionary. Matrix operations, fundamental to deep learning, become near-instantaneous: Traditional chips: O(n²) operations. Photonic chips: O(1) - parallel processing through optical interference. 1000×1000 matmuls in picoseconds. Where are we today? Real products are shipping: — Intel's 400G transceivers use silicon photonics. — Ayar Labs demonstrates 2Tb/s chip-to-chip links with AMD EPYC processors. Performance scales with wavelength count, not just frequency like traditional electronics. The manufacturing challenges are immense. — Current yield is ~30%. Silicon's terrible at emitting light and bonding III-V materials to it lowers yield — Temp control is a barrier. A 1°C change shifts frequencies by ~10GHz. — Cost/device is $1000s To reach mass production we need: 90%+ yield rates, sub-$100 per device costs, automated testing solutions, and reliable packaging techniques. Current packaging alone can cost more than the chip itself. We're 5+ years from hitting these targets. Companies to watch: ASML (manufacturing), Intel (data center), Lightmatter (AI), Ayar Labs (chip interconnects). The technology requires major investment, but the potential returns are enormous as we hit traditional electronics' physical limits.

Researchers from Columbia University and Cornell University recently reported a 3D-photonic transceiver that features 80 channels on a single chip and consumes only 120fJ/bit from its electro-optic front ends. The #transceiver achieves low energy consumption through low-capacitance 3D connections between photonics and co-designed #CMOS electronics. Each channel has a relatively low data rate of 10Gbps, allowing the transceiver's electronics to operate with high sensitivity and minimal energy consumption. The large array of channels compensates for the low per-channel data rates, delivering a high aggregate data rate of 800Gbps in a compact transceiver area of only 0.15mm2 (@5.3Tbps/mm2). In addition, having many low-data-rate channels relaxes signal processing and time multiplexing of data streams native to the processor. Furthermore, wavelength-division-multiplexing (#WDM) sources for numerous data streams are becoming available with the advent of chip-scale microcombs. The EIC is bonded to the PIC based on a 15μm spacing and a 10μm bump diameter (@25μm pitch) in an array of 2,304 bonds. This process mitigates two potential failure risks: 1) excessive tin causing flow and electrical short to adjacent bonds and 2) insufficient tin leading to brittle bonds. 👇Figure 1: a) An illustration of the 3D-integrated photonic-electronic system combining arrays of electronic cells with arrays of photonic devices. b) A microscope image of the 80-channel photonic device arrays with an inset of two transmitter and two receiver cells. c) Microscope images of the photonic and electronic chips. The active photonic circuits occupy an area outlined in white, while the outer photonic chip area is used to fan out the optical/electrical lanes for fiber coupling and wire bonding. The blue overlay shows a four-channel transmitter and receiver #waveguide path; the disk and ring overlays are not to scale. An inset shows a diagram of the fiber-to-chip edge coupler, consisting of a silicon nitride (Si3N4) inverse taper and escalator to silicon. d) A scanning electron microscope image of the bonded electronic and photonic chip cross-section. e) An image of the wire-bonded transceiver die bonded to a printed circuit board and optically coupled to a fiber array with a US dime for scale. f) A cross-sectional diagram of the electronic and photonic chips and their associated material stacks. Both chips consist of a crystalline silicon substrate, doped-silicon devices and metal interconnection layers. Daudlin, S. et al. Three-dimensional photonic integration for ultra-low-energy, high-bandwidth interchip data links. Nat. Photon. (2025).👉https://lnkd.in/gpeVGZna #SemiconductorIndustry #Semiconductor #Semiconductors #AI #HPC #Datacenter #Optics #Photonics #SiliconPhotonics #Optical #Networking #OCI #Ethernet #Infrastructure #Interconnect #CloudAI #AICluster AIM Photonics TSMC Defense Advanced Research Projects Agency (DARPA) #FiberCoupling #SiP

TSMC + Avicena: Reinventing Optical Interconnects Without Lasers As AI and HPC workloads explode, power-hungry copper links and complex laser-based optics are hitting their limits. Enter a game-changing collaboration: TSMC and Avicena are developing a MicroLED-based optical interconnect — no lasers, no modulators, just ultra-efficient light powered by CMOS integrated MicroLEDs. ▫️ Sub-pJ/bit energy efficiency ▫️ Simplified design using LED arrays instead of high-speed modulators ▫️ Short-to-medium reach (10–30m+), ideal for intra-rack AI GPU links ▫️ TSMC brings chiplet and CMOS image-sensor expertise to scale production ▫️ CPO vs. MicroLED LightBundle - CPO (Co-Packaged Optics): Relies on lasers, high-speed modulators, and fiber coupling, adding complexity, thermal constraints, and cost. - LightBundle (MicroLED): Uses direct-emitting MicroLEDs and imaging fibers — simpler, lower power (<1 pJ/bit), and easier to scale on-chip. Compared to CPO, the LightBundle solution can dramatically reduce system complexity, energy consumption, and cost, making it a strong candidate for next-gen AI infrastructure. 💡 This may not just be another interconnect, it’s a new class of optical I/O. It’s really worth watching. Reference source: [Avicena Press Release](https://lnkd.in/gqCkGUgq) Learn more: [IEEE Spectrum – TSMC’s MicroLED Optical Leap](https://lnkd.in/gVKpmP2a) #TSMC #Avicena #MicroLED #SiliconPhotonics #CPO #OpticalInterconnect #AIInfrastructure #Semiconductors #DataCenter #Chiplet #Photonics #TechInnovation #TSMCTech #AIHPC #CMOS #NextGenNetworking

Light-Based Quantum Leap: New Protocol Entangles Photons Without Measurement A Groundbreaking Step Toward Scalable Quantum Computing with Light In a major development for quantum physics and photonics, Georgia Tech researchers have proposed a new method to generate entanglement between photons—without relying on quantum measurement. This novel approach may overcome one of the key obstacles in building quantum computers that use light, opening the door to more scalable, reliable, and efficient quantum systems. How the New Protocol Works • The Core Innovation • Traditional methods for entangling photons rely on quantum measurements, which are probabilistic and often inefficient. • Georgia Tech’s protocol instead uses a geometric concept called non-Abelian quantum holonomy, allowing deterministic and consistent entanglement without measurements. • Why This Matters for Photonic Quantum Computing • Photons are ideal carriers of quantum information—they’re fast, stable, and immune to many forms of noise. • However, photons do not naturally interact with one another, making entanglement difficult. • This new method creates interaction-like behavior without requiring the photons to touch or interfere directly. • What Holonomy Enables • Holonomy is a geometric phase acquired by a quantum system over a closed path in its parameter space. • By leveraging non-Abelian holonomies, which depend on the order of operations, researchers can precisely control and entangle photon states. Key People and Publication • Led by Professor Chandra Raman from Georgia Tech’s School of Physics. • Postdoctoral researcher Aniruddha Bhattacharya emphasized the difficulty of photon interaction and the significance of overcoming it. • Findings were peer-reviewed and published in Physical Review Letters, a leading physics journal. Why This Discovery Is Important This innovation marks a critical advance in the pursuit of photonic quantum computers—systems that use light instead of matter-based qubits. Because photonic systems are naturally suited to long-distance communication and faster processing, this breakthrough could significantly accelerate the development of distributed quantum networks, secure communication channels, and large-scale quantum processors. By eliminating the need for quantum measurement during entanglement, the Georgia Tech protocol improves both the efficiency and reliability of quantum operations. This is a vital step toward realizing practical quantum computers that harness the speed and elegance of light. As research in quantum holonomy and photonic systems continues, this work lays foundational principles for a new generation of quantum technologies.

The explosive growth in computation and energy cost of artificial intelligence has spurred interest in alternative computing modalities to conventional electronic processors. Photonic processors, which use photons instead of electrons, promise optical neural networks with ultralow latency and power consumption. However, existing optical neural networks, limited by their designs, have not achieved the recognition accuracy of modern electronic neural networks. In a recent work published in Science Advances, we bridge this gap by embedding parallelized optical computation into flat camera optics that perform neural network computations during capture, before recording on the sensor. We leverage large kernels and propose a spatially varying convolutional network learned through a low-dimensional reparameterization. We instantiate this network inside the camera lens with a nanophotonic array with angle-dependent responses. The resulting setup is extremely simple: just replace a camera lens with our flat optics!! Combined with a lightweight electronic back-end of about 2K parameters, our reconfigurable nanophotonic neural network achieves 72.76% accuracy on CIFAR-10, surpassing AlexNet (72.64%), and advancing optical neural networks into the deep learning era. The paper can be found at: https://lnkd.in/gXUcn33Y

High-Speed, Efficient Photonic Memories "The researchers used a magneto-optical material, cerium-substituted yttrium iron garnet (YIG), the optical properties of which dynamically change in response to external magnetic fields. By employing tiny magnets to store data and control the propagation of light within the material, they pioneered a new class of magneto-optical memories. The innovative platform leverages light to perform calculations at significantly higher speeds and with much greater efficiency than can be achieved using traditional electronics. This new type of memory has switching speeds 100 times faster than those of state-of-the-art photonic integrated technology. They consume about one-tenth the power, and they can be reprogrammed multiple times to perform different tasks. While current state-of-the-art optical memories have a limited lifespan and can be written up to 1,000 times, the team demonstrated that magneto-optical memories can be rewritten more than 2.3 billion times, equating to a potentially unlimited lifespan." #optical #photonic

⚡️ Photonic processors to accelerate AI 🌟 Overview Researchers at MIT have created a breakthrough photonic processor that can execute the key operations of deep neural networks optically, on a chip. This innovation opens the door to unprecedented speed and energy efficiency, solving challenges that have held photonic computing back for years. 🤓 Geek Mode The heart of this advancement is the nonlinear optical function unit (NOFU), which enables nonlinear operations—essential for deep learning—directly on the photonic chip. Previously, photonic systems had to convert optical signals to electronic ones for these tasks, losing speed and efficiency. NOFUs solve this by using a small amount of light to generate electric current within the chip, maintaining ultra-low latency and energy consumption. The result? A deep neural network that trains and operates in the optical domain, with computations taking less than half a nanosecond. 💼 Opportunity for VCs This photonic processor isn't just a fascinating technical achievement; it’s a platform play. The ability to scale this technology using commercial foundry processes makes it manufacturable at scale and primed for real-world integration. For VCs, the implications are vast. Think lidar systems, real-time AI training, high-speed telecommunications, and even astronomical research—all demanding ultra-fast, energy-efficient computation. Startups and spinouts leveraging this tech could redefine edge computing, optical AI hardware, and next-gen telecommunications. 🌍 Humanity-Level Impact Beyond enabling faster AI, this chip represents a shift in how we think about computation itself. Energy efficiency at this scale could dramatically reduce the environmental footprint of AI, a growing concern as models become more resource-intensive. Additionally, real-time, low-power AI could unlock applications in disaster response, autonomous navigation, and scientific discovery, accelerating progress in areas that directly improve lives. It’s a step toward a future where technology works not only faster but smarter and more sustainably. Innovations like these highlight the extraordinary potential of human creativity—turning the impossible into the inevitable. The light-driven future of AI is closer than we think. 📄 Original paper: https://lnkd.in/ga8Bvubk #DeepTech #VentureCapital #AI #Photonics

Silicon hardware is hitting fundamental performance limits in terms of joules-per-flop. Do Data Centers get bigger indefinitely? No. We need a new computing substrate - here's a quick breakdown on the frontiers of computing physics: 1. Photonic computing – math at the speed of light Lightmatter ($850 M) | Celestial AI ($515 M) | Ayar Labs ($370 M) | Luminous Computing ($106 M) + Where it tops out now: Commercial cards such as Lightmatter Envise and China’s ACCEL chip routinely show 150–160 TOPS per watt, already ~5–6× an H100 GPU. Bench demos at Tsinghua reach >300 TOPS/W in small arrays. + Physics ceiling: The ultimate floor is the quantum shot-noise limit (~10 zJ per multiply-accumulate). With wavelength-division multiplexing, photonics can theoretically hit >10 PetaOPS/W for 8-bit MACs before quantum noise dominates. + Near-term chokepoints: on-chip lasers burn static power; modulators and ADCs still sit in CMOS; waveguide footprints (µm scale) cap density to ~10 M “neurons” per cm². + Road-map potential (5-15 yr): moving lasers off-chip + tighter 3D photonic-electronic stacks should enable >1 POPS/W inference boxes and slash DRAM traffic by using light for rack-scale interconnect. 2. Analog-in-memory AI – move the compute to the data EnCharge AI ($144 M) | Mythic ($178 M) + Where it is: EnCharge AI boards deliver 150 TOPS/W today; a finer process taped-out this spring hit 650 TOPS/W in lab silicon. Fanatical Futurist Fundamental limits: Energy is set by charge-discharge of tiny capacitors in SRAM cells; at the Landauer limit (kT ln2) that’s ≈ 3 zJ per 8-bit MAC. Practical noise/ADC overhead pushes the floor to ~100 zJ, so 1 PetaOPS/W is technically reachable. + Bottlenecks: ADC/DAC slices still dominate power at high precision, and device mismatch drifts with temperature. Path forward: 6-8-bit “good-enough” networks + periodic digital recalibration, plus stacking compute-SRAM layers, point to sustained 10× efficiency every ~5 years. + SRAM or flash arrays perform MACs in-situ; prototypes reach 150 TOPS/W. Eliminates up to 90 % of the energy now wasted shuttling weights to cores. 3. Neuromorphic chips – brains in silicon BrainChip (AU-listed, A$21 M raise) | SynSense ($43 M | Innatera ($43 M) Tracxn +State of the art: Intel’s Hala Point cluster (1.15 B neurons) delivers 20 peta-spike-ops/s at >15 TOPS/W on dense CNNs and 100× CPU efficiency on sparse sensory tasks. + Hard stops: Event-driven logic can, in principle, drop to a single spike ≈ 40 zJ, but wire capacitance and leakage in sub-threshold CMOS set a floor near 1 aJ/spike. That still implies >100 PetaOPS/W in large, sparse nets. + Key hurdles: compiling mainstream transformer models into spike form; on-chip learning algorithms; network-on-chip congestion at billion-neuron scale.

Researchers have made a significant breakthrough in AI hardware with a 3D photonic-electronic platform that enhances efficiency and bandwidth, potentially revolutionizing data communication. Energy inefficiencies and data transfer bottlenecks have hindered the development of next-generation AI hardware. Recent advancements in integrating photonics with electronics are poised to overcome these challenges. 💻 Enhanced Efficiency: The new platform achieves unprecedented energy efficiency, consuming just 120 femtojoules per bit. 📈 High Bandwidth: It offers a bandwidth of 800 Gb/s with a density of 5.3 Tb/s/mm², far surpassing existing benchmarks. 🔩 Integration: The technology integrates photonic devices with CMOS electronic circuits, facilitating widespread adoption. 🤖 AI Applications: This innovation supports distributed AI architectures, enabling efficient data transfer and unlocking new performance levels. 📊 Practical Quantum Advancements: Unlike quantum entanglement for faster-than-light communication, using quantum physics to boost communication speed is more feasible and practical. This breakthrough is long overdue, but the AI boost might create a burning need for this technology. Quantum computing might be seen as a lot of hype, but using advanced quantum physics to enhance communication speed is more down-to-earth than relying on quantum entanglement for faster-than-light communications, which is short-lived #AI #MachineLearning #QuantumEntanglement #QuantumPhysics #PhotonicIntegration #SiliconPhotonics #ArtificialIntelligence #QuantumMechanics #DataScience #DeepLearning

Speed is everything in computing. From AI training to financial transactions, processing power defines what’s possible. But traditional semiconductor-based computing is hitting its limits—slower speeds, higher energy consumption, and physical constraints. Enter light-speed computing, a breakthrough powered by photonic technology, which replaces electrons with photons, allowing data to travel at the speed of light. Why Light-Speed Computing Matters Traditional computing generates heat, resistance, and high power consumption. Photonic computing eliminates these issues, delivering: ✅ Faster AI Processing – AI models will train and infer in record time ✅ Lower Energy Consumption – Data centers will run more efficiently ✅ More Powerful Applications – Real-time AI, finance, and research breakthroughs Industries That Will Benefit Most 🚀 AI & Machine Learning – Faster model training & real-time AI applications 💰 Finance & Trading – High-frequency trading (HFT) will become even more powerful 🧬 Healthcare & Biotech – Faster genome sequencing & drug discovery ☁️ Cloud & Data Centers – Reduced operational costs & energy use 🔬 Scientific Research – Climate modeling, space exploration & quantum physics Challenges & Future Adoption 🔹 Manufacturing Complexity – Photonic chips require specialized production 🔹 Industry Adoption – Companies need to integrate light-speed tech into existing systems 🔹 Software Optimization – New programming frameworks must be developed Despite challenges, major tech companies are investing heavily in silicon photonics & optical computing, signaling that this revolution is closer than we think. Final Thoughts Light-speed computing isn’t just about speed—it’s about transforming what’s possible. As we move towards photonic-powered AI and real-time computing, we are on the verge of a new technological era. 💡 What are your thoughts on light-speed computing? Let’s discuss! #Technology #Innovation #Computing #AI #Photonics #FutureTech #MachineLearning #CloudComputing #quant