Silicon photonics is revolutionizing neuromorphic computing by enabling ultrafast and energy-efficient neural network implementations. Recent studies have demonstrated optical neural networks with processing speeds exceeding 100 teraflops per second, outperforming traditional electronic systems by orders of magnitude. These systems leverage wavelength-division multiplexing (WDM) to achieve parallel processing across multiple channels, significantly increasing throughput.
The integration of phase-change materials (PCMs) with silicon photonic circuits has enabled non-volatile synaptic weight storage in neuromorphic systems. Researchers have achieved write speeds below 10 nanoseconds and endurance cycles exceeding 10^12, making PCM-based photonic synapses highly reliable for long-term operation. This approach mimics biological synapses more closely than electronic counterparts, offering a more natural implementation of neural learning algorithms.
Energy efficiency is a key advantage of silicon photonics in neuromorphic computing. Optical interconnects consume less than 1 femtojoule per bit per millimeter, compared to tens of picojoules for electrical interconnects. This reduction in energy consumption is critical for deploying large-scale neuromorphic systems in data centers and edge devices, where power constraints are stringent.
Recent advancements in on-chip laser integration have further improved the practicality of silicon photonic neuromorphic systems. By embedding III-V semiconductor lasers directly onto silicon substrates, researchers have achieved continuous-wave operation with output powers exceeding 10 milliwatts and wall-plug efficiencies above 20%. These developments reduce reliance on external light sources, simplifying system design and lowering costs.
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