Advancements in Photonics

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.

MIT Unveils AI Chip That Operates Entirely on Light, Not Electricity Researchers at MIT have created a revolutionary AI accelerator chip that performs computations entirely using light rather than electricity potentially slashing energy consumption in data centers by over 90%. This photonic AI chip leverages arrays of nano-optic waveguides and micro-ring modulators to process data using beams of modulated light. At its core, the chip replaces electrical transistors with tiny optical interference units that manipulate light’s phase and amplitude. Matrix multiplications, the backbone of neural networks, are executed as light passes through a mesh of these units, eliminating resistive heating entirely. The chip has no moving parts and transmits information at the speed of light, literally. Initial tests showed the photonic processor performing convolutional neural network (CNN) tasks at 10 teraflops per watt far surpassing Nvidia’s top-tier GPUs. What’s more, it generates no heat beyond the laser source itself, drastically simplifying cooling and thermal design. MIT’s prototype uses silicon photonics and is fully compatible with existing CMOS processes, making it scalable for commercial production. Future versions may be paired with on-chip photonic memory, enabling entirely light-driven inference systems. The team envisions hyperscale data centers running vast language models on these chips with almost no electricity use, ushering in a post-electronic computing era. Note: The opinions expressed here are solely my own and do not represent my employer.

Optical AI Chip Delivers Hundredfold Leap in Speed and Energy Efficiency Introduction Chinese researchers have unveiled a photonic artificial intelligence chip that dramatically outperforms today’s leading electronic AI hardware. By using light instead of electrons to process information, the new system demonstrates how generative AI workloads could be executed far faster and with radically lower energy consumption Core Breakthrough LightGen Optical Chip • The LightGen chip was developed by teams from Shanghai Jiao Tong University and Tsinghua University. • It integrates more than 2 million photonic neurons on a compact 136.5 square millimeter chip. • The system performs AI computations using laser pulses, operating at the speed of light rather than electrical currents. Performance Gains • LightGen achieved a computing speed of 3.57×10⁴ tera operations per second. • Energy efficiency reached 6.64×10² tera operations per second per watt. • Overall performance exceeded Nvidia’s A100 AI accelerator by more than 100 times in both speed and energy efficiency for generative tasks. Architectural and Algorithmic Advances • Researchers designed an optical latent space that compresses and reconstructs data efficiently, enabling complex generative operations. • A novel unsupervised training algorithm eliminated the need for massive labeled datasets. • The approach allows the chip to learn statistical patterns in data in a manner closer to human learning. Demonstrated Capabilities • High-resolution semantic image generation at 512×512 pixels • Image denoising and style transfer • Three-dimensional image generation and manipulation • Video and complex scene synthesis with logical consistency Why Photonic Computing Matters • Electronic chips are approaching physical and thermal limits as AI workloads grow. • Optical signals reduce power consumption and latency while enabling extreme parallelism. • Previous photonic systems struggled with complex generative tasks, a barrier LightGen overcomes. Why This Matters LightGen signals a potential inflection point in AI hardware design. By proving that photonic computing can independently handle sophisticated generative AI workloads with orders-of-magnitude gains in efficiency, the research offers a credible path to addressing AI’s rapidly escalating energy demands. If scalable, optical AI chips could reshape data centers, accelerate creative AI applications, and redefine the balance between performance and sustainability in next-generation computing. I share daily insights with 36,000+ followers across defense, tech, and policy. If this topic resonates, I invite you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

Scientists have demonstrated that a common thermoplastic can be transformed into a miniature light analyzer — capable of splitting light into a spectrum, much like a lab-grade spectrometer. By engineering microstructures of around 10 × 10 micrometers inside the polymer, researchers enabled the material to separate light into spectral components across a wide range (~400–1550 nm), spanning visible to near-infrared wavelengths. In essence, the material itself functions as a mini spectrometer. What makes this breakthrough particularly compelling is the fabrication method. Using ultrashort laser pulses, scientists create microscopic vortex-like structures within the plastic. These nano- and micro-scale features interact with light, enabling spectral decomposition — an approach rooted in nanophotonics. Why this matters: • Spectrometers could be directly integrated onto microchips • Light sensors may become standard in smartphones and wearable devices • Chemical and material analysis tools could become significantly smaller and more affordable • Compact devices could enable microscopic spectral imaging Perhaps most remarkably, this functionality requires no moving optical parts or complex calibration — it emerges purely from the internal geometry of the material. This is a powerful signal of where photonics is heading: optical functionality is increasingly embedded within the structure of materials themselves, rather than relying on discrete components like lenses or prisms. The result? A new generation of compact “lab-on-a-chip” systems for analyzing light and matter. #Photonics #Nanophotonics #DeepTech #MaterialsScience #Innovation #Light #Thermoplastic #FutureTech #Optics #Spectroscopy #Microfabrication #TechTrends #Spectrometer #Spectrum

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

The way we move data inside our chips is hitting a limit… Moving data with electrons is simply too heavy for the next generation of computing. Every time we push electricity through metal wires it creates friction. If we try to make chips move data any faster the resistance creates enough heat to melt the silicon. This is why AI power consumption is spiraling. Lightmatter found the solution…. Their platform called Passage replaces copper wires with Silicon Photonics. Instead of electricity it uses beams of light moving through microscopic glass tunnels. • Zero Mass: Photons move with no resistance and virtually no heat. • 100x Faster: Their M1000 chip moves 114 Terabits of data per second. This dwarfs traditional electronic interconnects. • Green AI: We can finally scale AI models to be 1000x smarter without overloading the global power grid. The future of computing is not just about smaller transistors. It is about moving at the speed of light!! 📚 Resources and Learn More • Lightmatter Official Press: “Lightmatter Unveils Passage M1000 Photonic Superchip” (March 2025). • Hot Chips 2025 Presentation: Darius Bunandar, “Passage M1000: 3D Photonic Interposer for AI.” • Lightmatter Technical Blog: “Seeing is Believing: A Technical Deep Dive into Lightmatter Hardware” (September 2025). • HPCwire Analysis: “Lightmatter Aims to Leapfrog I/O Limitations with 3D Photonic Interconnect” (December 2025). #techexplained #futuretech #ai

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

Silicon Photonics in 2026: The Shift From Trend to Transition LightCounting’s forecast—over 50% of optical transceiver sales using silicon-photonics modulators in 2026 up from 10% in 2018—represents a dramatic industry inflection. This shift is being driven by four major forces: ✅ 1. Explosive Bandwidth Demand from AI Clusters AI workloads (ChatGPT-class models, large-scale training clusters, hyperscale inference) require: • 800G → 1.6T optical transceivers • low power / low-latency interconnects • tight integration between compute and optics Electrical interconnects saturate around a few centimeters at >100 Gbps. Silicon photonics eliminates these physical limits, enabling co-packaged optics and eventually optical I/O directly integrated with advanced packaging. ✅ 2. Foundries Reconfiguring Their Roadmaps for SiPh The foundry landscape is shifting from small experimental lines to full commercial 300 mm manufacturing. The table you shared captures this transformation. ✅ 3. Wafer Transition: 200 mm → 300 mm This is one of the biggest structural shifts. Why 300 mm matters: • Better uniformity of waveguides and modulators • Higher yield for photonic components • Economies of scale similar to CMOS • Better compatibility with advanced packaging As transceiver volumes scale with AI datacenters, 200 mm lines (like Tower’s current base) cannot meet hyperscale demand. Most commercial deployment in 2026+ will rely on 300 mm. ✅ 4. Packaging Becomes the Real Battlefield Silicon photonics != complete system The real bottleneck is packaging and fiber alignment. Three major approaches are emerging: 1. Co-Packaged Optics (CPO) Optical engines integrated beside switch ASICs. TSMC and Nvidia are pushing this. 2. Pluggable Transceivers Using SiPh Still dominant today (800G / 1.6T). GF and Intel lead here. 3. Optical I/O / Optical Chiplets Future vision — optical communication directly connected to compute tiles. This requires: • ultra-low-loss coupling • integrated lasers or hybrid bonding • photonic + electronic co-design Expect early pilot deployments around 2027–2028.

 Spectroscopy: The Science of Light-Matter Interaction Spectroscopy is more than just an analytical tool—it’s a gateway to understanding matter at the atomic and molecular level. By studying how electromagnetic radiation interacts with substances, we can decode valuable information about their composition, structure, and electronic properties. 🔹 The Fundamental Mechanism of Spectroscopy At its core, spectroscopy involves: 1️⃣ Absorption, Emission, or Scattering of Light – When light (electromagnetic radiation) interacts with matter, certain wavelengths are absorbed, emitted, or scattered depending on the material's energy states. 2️⃣ Energy Transitions – Electrons, molecules, or nuclei transition between energy levels when they absorb or emit photons. These transitions are governed by quantum mechanics. 3️⃣ Spectral Analysis – The resulting spectrum—a unique fingerprint of the substance—is analyzed to determine structural and compositional details. 🔹 Key Components of a Spectroscopic System A typical spectroscopic setup includes: 📌 Radiation Source – Provides the necessary electromagnetic waves (e.g., UV lamp, laser, X-ray tube). 📌 Monochromator or Dispersive Element – Filters and selects specific wavelengths (e.g., prisms, diffraction gratings). 📌 Sample Holder – The medium where light interacts with the analyte (e.g., cuvettes, fiber optics). 📌 Detector – Converts light signals into readable data (e.g., photomultiplier tubes, charge-coupled devices). 📌 Data Processing System – Analyzes the intensity and wavelength of absorbed/emitted radiation to interpret results. 🔹 Types of Spectroscopy & Industrial Applications 🔸 UV-Visible Spectroscopy (UV-Vis) – Quantifies concentration based on electronic transitions; used in pharmaceuticals and environmental monitoring. 🔸 Infrared Spectroscopy (IR & FTIR) – Identifies molecular vibrations; widely applied in polymer, food, and forensic sciences. 🔸 Raman Spectroscopy – Studies molecular vibrations via inelastic scattering; critical in material science and nanotechnology. 🔸 Nuclear Magnetic Resonance (NMR) Spectroscopy – Explores atomic environments using nuclear spin properties; essential in organic chemistry and drug discovery. 🔸 X-ray Spectroscopy (XRF, XPS) – Analyzes elemental composition and oxidation states; crucial for metallurgy and semiconductor research. With advances in AI-powered spectral analysis and portable spectrometers, spectroscopy is evolving beyond the lab, making real-time diagnostics and in-field chemical analysis more accessible than ever. Have you used spectroscopy in your industry? What challenges or breakthroughs have you encountered? Let’s discuss in the comments! 👇 #spectroscopy #ndt #ndtanalysis #engineering #technology #quality #qa #qc #materialanalysis

The #AI race is often framed around chips, models, and compute. But there is a quieter layer that will decide who truly scales. #Connectivity. As global capex in AI data centres moves toward $645 billion, the conversation is shifting from “how much compute” to “how efficiently that compute talks to itself.” Because in modern AI systems, performance is no longer limited by processing power alone. It is constrained by how fast, how reliably, and how intelligently data moves. And that is where interconnect becomes the real multiplier. As data centres scale, their architecture is no longer linear. It becomes dense, distributed, and deeply interdependent. Every GPU cluster, every storage layer, every inference engine depends on seamless communication. This creates three compounding pressures. First, signal integrity becomes critical. As connections increase, maintaining clean transmission becomes harder. Second, latency becomes strategic. Milliseconds now influence model efficiency, cost, and user experience. Third, cabling complexity scales non-linearly. More nodes mean exponentially more interconnections. This is why the connector and interconnect ecosystem is moving from a supporting role to a strategic layer. The debate is not optical versus copper. It is about where each fits best. Optical brings long distance, high bandwidth, and resistance to interference, but at a higher cost for short links. Copper delivers efficiency for short-range, low latency communication, but struggles at scale over distance. Future data centres will not choose one. They will design hybrid architectures where copper dominates inside racks and optical connects across racks and clusters. The advantage will come from integration, not substitution. Value is now concentrating across layers: optical chips, DSPs, modules, fibers, silicon photonics, power connections, and network switching. Players like Broadcom and Marvell in connectivity chips, Coherent and Lumentum in optical components, Corning in fiber, and TSMC enabling photonics manufacturing are positioning across this stack. The signal is clear. The next wave of value will not sit with a single component. It will sit with those who control multiple layers of the interconnect stack. Leaders should not miss five shifts. Compute without connectivity is stranded capital. Latency is now a cost driver. Design thinking is replacing component thinking. Supply chain depth matters more than ever. Interconnect is now a boardroom topic. My core inference is simple. The winner in AI infrastructure will not be the one with the most compute, but the one with the most efficient movement of compute. This shifts advantage from scale alone to design intelligence. In every industrial shift, one layer quietly compounds value. In AI, that layer is no longer just silicon. It is how silicon connects. DC* Dinwins Sources: Moomoo Insights, industry analysis on AI data center interconnect demand