Silicon Photonics Technology Advancements

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

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.

NVIDIA, TSMC Develop Advanced Silicon Photonic Chip Prototype NVIDIA and TSMC reportedly developed a groundbreaking silicon photonics-based chip prototype at the end of last year, blending photonic circuits with traditional ones to tackle the limitations of semiconductor fabrication. Silicon photonics replaces electrons with photons for communication within chips, enabling faster data speeds and higher bandwidth without requiring ultra-advanced manufacturing techniques. This innovation comes alongside their work on optoelectronic integration and advanced packaging technologies, addressing critical constraints in AI chip performance, cost, and supply. As TSMC remains NVIDIA’s primary manufacturing partner, this collaboration underscores their push to overcome scaling challenges and redefine AI chip capabilities. My Take Silicon photonics can potentially redefine Moore’s Law for the AI era, focusing on architectural innovation rather than just transistor density. This shift could unlock new possibilities for edge computing and AI applications, driving unprecedented efficiency in data processing. Silicon photonics can deliver 10–50x higher bandwidth and up to 90% lower power consumption for data transfer. #NVIDIA #TSMC #SiliconPhotonics #AIChips #Semiconductors #AdvancedPackaging #Innovation #TechnologyLeadership Link to article: https://lnkd.in/eMTMcpD6 Credit: Wccftech This post reflects my own thoughts and analysis, whether informed by media reports, personal insights, or professional experience. While enhanced with AI assistance, it has been thoroughly reviewed and edited to ensure clarity and relevance. Get Ahead with the Latest Tech Insights!  Explore my searchable blog: https://lnkd.in/eWESid86