Photonic Neural Processor

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📚 Glossary

Photonic Neural Network

An artificial neural network that uses optical signals (photons) instead of electrical signals (electrons) to perform computations, enabling speed-of-light processing.

Mach-Zehnder Interferometer (MZI)

An optical device that splits a light beam into two paths and recombines them; by controlling the relative phase, it can perform the matrix operations needed for neural networks.

Matrix Multiplication

The core mathematical operation in deep learning, where input data is transformed by weight matrices; photonic processors perform this operation at the speed of light.

Optical Nonlinearity

Non-proportional response of a material to light intensity, essential for implementing activation functions in photonic neural networks -- a key challenge in the field.

Silicon Photonics

The technology of building optical circuits on silicon chips using standard semiconductor fabrication, enabling mass production of photonic processors.

Wavelength Division Multiplexing

Sending multiple data channels simultaneously through the same optical path using different wavelengths of light, dramatically increasing throughput.

Microring Resonator

A compact circular optical waveguide that can filter, switch, and modulate light signals, used as programmable weights in photonic neural networks.

Phase-Change Material (PCM)

A material that can switch between crystalline and amorphous states, used in photonic systems for non-volatile weight storage in neural network synapses.

Optical Computing

Computing paradigm that uses photons for information processing, offering advantages in speed, parallelism, and energy efficiency over electronic computing.

Activation Function

A nonlinear mathematical function applied to neural network outputs; implementing this optically (without converting to electronics) is a major challenge.

In-Situ Training

Training a neural network directly on the photonic hardware rather than on a digital computer, enabled by the ultralow latency of optical computation.

Neuromorphic Photonics

The field combining neuroscience-inspired computing with photonics, using light-based neurons and synapses to mimic biological neural processing.

Photonic Integrated Circuit (PIC)

A chip that integrates multiple optical components (waveguides, modulators, detectors) on a single substrate, analogous to electronic integrated circuits.

🏆 Key Figures

Yichen Shen (2017)

Led the 2017 MIT team that demonstrated the first on-chip optical neural network using 56 programmable Mach-Zehnder interferometers on a silicon photonic chip

Dirk Englund (2024)

MIT professor whose Quantum Photonics and AI Group demonstrated a fully integrated photonic processor achieving 92%+ accuracy in sub-nanosecond inference (2024)

Marin Soljacic (2017)

MIT professor who co-led the pioneering 2017 optical neural network work and continues advancing photonic computing architectures

Demetri Psaltis (1990s)

Pioneer of optical neural networks using holography in the 1990s at Caltech, establishing the theoretical foundations for photonic computing

Wolfram Pernice & Harish Bhaskaran (2019-2021)

Developed phase-change material-based photonic synapses enabling non-volatile weight storage for photonic neural networks

Bhavin Shastri & Paul Prucnal (2021)

Published influential Nature Photonics review establishing the roadmap for neuromorphic photonics and spike-based optical computing

Gu Min (2025)

Led comprehensive reviews of integrated photonic synapses, neurons, and memristors at the University of Shanghai for Science and Technology

💬 Message to Learners

The future of AI computing might not be faster electronics -- it might be light itself. Photonic neural processors represent a fundamental shift: instead of pushing electrons through tiny wires that generate heat and waste energy, we guide photons through optical circuits that compute at the speed of light. A single photonic chip can perform in nanoseconds what electronic processors take microseconds to complete. As AI models grow ever larger and more power-hungry, photonic computing offers a path to sustainable, ultrafast intelligence. The transistor revolution changed the world; the photonic revolution may change it again.