🌐 Module 8 · Context and the Future · Chapter 8.4 · 9 min read

Y100 Photonics Vision

From electron to photon — SIDRA's 2031 chip.

Prerequisites

What you'll learn here

  • Explain photonic compute-in-memory
  • Compare optical MVM to electrical MVM
  • State silicon-photonics technology's current status
  • Detail Y100 photonic spec targets
  • Identify Türkiye's position in the post-2030 photonic AI race

Hook: Computing with Light

Y1-Y10: electrical memristor crossbar. Peak 300 TOPS/W.

Y100 target: photonic MVM. 1000-10000 TOPS/W. Computing at light speed.

This chapter opens the photonic vision.

Intuition: Photon Beats Electron

Photons beat electrons on:

  • Propagation speed: light speed (2× electron drift).
  • No resistance → no IR drop.
  • Parallel wavelengths (WDM) → one waveguide carries 100 signals.
  • Low thermal effects.

Downsides: physical size large (wavelength ~1 µm), modulation slow (~50 GHz).

Formalism: Silicon Photonics + MVM

L1 · Başlangıç

Silicon photonics:

Optical devices in existing silicon fabs:

  • Waveguide: Si strip carries photons.
  • Modulator: electrical signal → photon amplitude.
  • Photodetector: photon → electrical.
  • Mach-Zehnder interferometer: two waves interfere → output controlled.

TSMC, Intel, GlobalFoundries have silicon-photonics services.

Photonic MVM:

MZI mesh:

  • N×N matrix = N×N Mach-Zehnder interferometers.
  • Each MZI tunable (with heater).
  • Input: N optical waves (wavelength division).
  • Output: N waves carrying the MVM result.

Speed:

  • MZI settle: 1 µs (heater thermal).
  • Light transit: 100 ps.
  • Modulation: 50 GHz.

Energy:

  • MZI heater: ~10 mW on.
  • Modulator: ~1 pJ/bit.
  • Photodetector: ~0.1 pJ/bit.

Y100 photonic target:

  • 4096×4096 MZI matrix.
  • 16M MACs/step.
  • ~10 ns/MVM.
  • ~1 TOPS/mJ = 1000 TOPS/W theoretical.
L2 · Tam

Hybrid: memristor + photonic:

Pure photonic is hard (heaters slow, no non-volatility).

Hybrid approach:

  • Weights in memristors (non-volatile, 256 levels).
  • MVM in photonics (fast, efficient).
  • Conversion: memristor → MZI heater control.

Y100 design: MZI mesh at the head of each crossbar. Electron + photon together.

Bandwidth:

Photonic waveguide: 100 channels × 100 Gbps = 10 Tbps. Electrical Cu wire: 100 Gbps max.

100× bandwidth advantage. Critical for chiplet-to-chiplet communication.

Heat:

MZI heaters run hot. 1000 MZIs × 10 mW = 10 W. Within Y100’s 100 W.

Alternative: electro-optic modulation (faster, cooler). MEMS also a candidate.

Thermal drift:

Temperature shifts MZI phase. Extra temperature-control circuitry.

Manufacture:

TSMC 7 nm + silicon photonics. Intel / GlobalFoundries also capable.

Türkiye: UNAM + BİLGEM silicon-photonics research exists. Y100 needs industrial-scale.

L3 · Derin

Rival photonic AI companies:

Lightmatter (US): Photonic AI chip. $300M funding. 2024 product. PsiQuantum (US): Quantum + photonic. Rain AI (US): Analog + photonic. Luminous (US): Silicon-photonics AI. Xanadu (Canada): Quantum-photonic.

Türkiye: none. Open territory.

Tech trends:

  • Integrated photonics: no longer a separate chip; CMOS + optics on one die.
  • Co-packaged optics (CPO): photons + electrons inside the package.
  • Neuromorphic photonics: spike-based photonic (next-gen).

Y100 timeline:

  • 2026: photonics research begins (UNAM + METU).
  • 2028: MZI prototype (FPGA-emulated).
  • 2030: silicon-photonics test chip (TSMC).
  • 2031-2033: Y100 photonic product.
  • 2035+: Y100 generation widespread.

Critical for Türkiye:

The photonic boom is just starting. Within 5 years the US + China will lead. If Türkiye enters now, it can be a player 2030-2035 in photonic AI.

Missed: SIDRA Y100 loses its global leadership potential.

Energy savings:

Y100 photonic: 100 W, 3 PFLOPS. NVIDIA B300 (post-2026 est.): 1500 W, 20 PFLOPS. SIDRA 5× more efficient (FLOP/W).

At datacenter scale: 50% electricity savings. Large drop in global AI climate load.

Challenges:

  • Yield: photonic manufacturing is hard (target >10%).
  • Ecosystem: silicon-photonics design tools scarce.
  • Talent: photonic engineers few in Türkiye.
  • Investment: Y100 requires $1B+.

Experiment: Y100 vs H100 in 2030

NVIDIA B300 (2030 estimate, GPU):

  • 2000 W, 50 PFLOPS.
  • 25 TOPS/W.
  • $50K/chip.

SIDRA Y100:

  • 100 W, 3 PFLOPS.
  • 300 TOPS/W (electronic) + photonic 1000+ TOPS/W.
  • $2K/chip.

Compare:

  • FLOP/W: SIDRA 10-40× more efficient.
  • Cost: SIDRA 25× cheaper.
  • Speed (FLOPS): NVIDIA 16× faster (for big training).

Use:

  • Datacenter training: NVIDIA.
  • Datacenter inference: SIDRA Y100 (energy + cost).
  • Edge: SIDRA dominates.

Market forecast 2033:

  • NVIDIA GPU: $100B/year.
  • SIDRA (global) $5B/year (5%).

For Türkiye: enormous. Globally: niche. Precisely targeted niche market.

Quick Quiz

1/6Y100 photonic advantage?

Lab Exercise

Y100 photonic investment plan.

2026-2028: Foundations.

  • UNAM + METU silicon photonics R&D.
  • 5 PhD students.
  • $2M fund.

2028-2030: Prototype.

  • TSMC silicon-photonics MPW ($500K).
  • FPGA emulator.
  • 20 engineers.

2030-2033: Product.

  • Full fab + photonics ($1B investment).
  • 200+ engineers.
  • First Y100 shipments 2031.

2033+: Scale.

  • Global market.
  • $5B/year revenue.

Total 10-year investment: $2B. ROI: 5-10 years.

Large for Türkiye but realistic strategic investment.

Cheat Sheet

  • Y100 hybrid: memristor + silicon photonics.
  • Photonic MVM: MZI mesh, light-speed.
  • Target: 3 PFLOPS, 100 W, 300-1000 TOPS/W.
  • Rivals: Lightmatter, Rain, Luminous — none in Türkiye.
  • Timeline: 2031-2033 product.
  • Investment: $2B over 10 years.

Vision: The Photonic Era

  • Y100 (2031): hybrid photonic prototype.
  • Y200 (2033): fully photonic MVM.
  • Y1000 (2040+): photonic + quantum + bio-compatible triad.

AI’s physics revolution. Türkiye’s chance to be in the room.

Further Reading

  • Next chapter: 8.5 — Your Place
  • Previous: 8.3 — Ethics
  • Silicon photonics: Reed, Silicon Photonics, Wiley.
  • Lightmatter: lightmatter.co official.
  • Photonic AI: Shen et al., Deep learning with coherent nanophotonic circuits, Nature Photonics 2017.