- Comparison of Physical Characteristics
A detailed comparison table outlines the fundamental differences between electronic and photonic signal transmission technologies across several dimensions:
| Dimension | Electrons (typical) | Photons (typical) |
| Speed | Drift velocity ~10⁻⁴ m/s in copper wires | ~3×10⁸ m/s (speed of light in medium) |
| Bandwidth | Up to ~100 Gb/s (SerDes over copper) | Up to 1.6 Tb/s per mm², 3.2 Tb/s per port, 400 Tb/s total |
| Energy/Bit | ~0.3–10 pJ/bit | ~59 fJ/bit (Si-photonics), <100 fJ/bit (VCSEL) |
| Integration Density | ~170 million transistors/mm² (TSMC 5nm) | ~5.3 Tb/s/mm² (photonic 3D channel density) |
| Manufacturing Cost | ~€10,700/mm² at 28 nm node | ~$2,860/cm² for passive PICs (20 chips of 25 mm²) |
2. Principles of Digital and Neuromorphic Signal Processing
🔹 Digital Signal Processing
- Uses rising/falling signal edges as triggers.
- Latches operate as binary states:
- 1 → between 2.0 V and 5.0 V
- 0 → between 0 V and 0.8 V
- More flexible analog voltage levels can be held using Sample & Hold circuits.
🔹 Neuromorphic Event Processing
- Utilizes analog/mixed-signal processing with electrons based on:
- Addition: Kirchhoff’s current law.
- Multiplication: Ohm’s law (I = V × G).
🔹 Photon-Based Analog Processing
- Can mimic similar principles:
- Addition: Using lenses to focus beams.
- Multiplication: Varying intensity = brightness.
3. Physical Scaling: Electronics vs. Photonics
| Metric | Electronics | Photonics |
| Scaling limit | Transistor size: ~2–3 nm (~20–30 Å) | Limited by wavelength of light (~100× larger) |
| Size unit | nm (feature size) | nm (wavelength) |
| Component Size | Nanoscale | Limited by wavelength / refractive index |
4. Wavelength Dependency & Material Transparency
- The refractive index (n) is wavelength-dependent, meaning different colors (frequencies) of light bend differently:
- Blue light: shorter λ → high n → more strongly refracted.
- Red light: longer λ → low n → less refraction.
- Silicon becomes transparent at wavelength λ ≈ 1300–1600 nm, making this range ideal for silicon photonics.
5. Conclusion
The document provides a concise yet detailed technical overview of the performance and physical trade-offs between electronic and photonic systems. It emphasizes the superior bandwidth and energy efficiency of photonics, but also highlights current limitations in integration density and component size due to physical wavelength constraints.
Photonics, especially in silicon-based systems, is increasingly used for ultra-fast interconnects, neuromorphic computing, and optical signal processing, with strong momentum from industry players (e.g. NVIDIA, AIM Photonics).