Fundamental Divergence: Photonic Computing Vs. Telecommunications Photonics

By John Oncea, Editor

Photonic computing and telecommunications photonics optimize for fundamentally opposing requirements – latency versus distance, reconfigurability versus stability, nonlinearity versus linearity – demanding different materials, devices, and design philosophies.

The ongoing transition from photonics in telecommunications to photonics in computing represents far more than a change of application. It signals a fundamental reorientation of priorities, performance metrics, and physical design rules. Photonic computing does not merely inherit the toolbox of telecom photonics; it rewrites the optimization logic that shaped it.

The Distance-To-Latency Paradigm Shift

Telecommunications photonics originated around a singular challenge: transmitting information across continents without degradation. The central metric was link distance and reliability, not absolute latency. Systems were designed to minimize signal loss across hundreds or thousands of kilometers. Dispersion-managed fibers, erbium-doped fiber amplifiers, and forward error correction all evolved from this imperative to maximize distance and reliability.

According to MDPI, latency does matter in specific telecom applications, particularly for high-frequency trading, where hollow-core fiber provides approximately 31% latency reduction compared to conventional fiber, and in emerging data center interconnects, adds OFS. However, latency remains secondary to reach and robustness in most long-haul telecommunications systems.

Photonic computing inverts that paradigm. Distances shrink from kilometers to millimeters, and while propagation delay becomes secondary to device and control latency rather than truly negligible, what matters now is time, specifically, the light’s transit time through logic elements. Each optical path, modulator, and resonator must execute a compute operation in nanoseconds or less. In neural or analog photonic processors, this latency directly defines the achievable throughput.

Such compressed time constants demand ultrafast device dynamics. The signal-path dynamics of photonic accelerators operate in the picosecond-to-nanosecond regime. However, according to Nature, weight and control updates follow different timescales. Thermo-optic tuning typically operates in the microsecond to millisecond range, with recent advances achieving sub-microsecond reconfiguration speeds of approximately 85 nanoseconds for optimized microheaters, according to Academia.

Commercial systems currently work with millisecond reconfiguration times, targeting microsecond regimes in future generations, according to Electro Optics. Even high-speed telecom Mach-Zehnder modulators operating at 100 GHz serve different purposes than the extremely high analog bandwidths required in photonic computing, where the performance metric centers on processing throughput rather than per-weight update rates.

Equally transformative is the energy-per-bit metric. Long-haul links can afford milliwatts per gigabit since power is amortized over vast distances. A photonic computing core, performing trillions of multiply-accumulate operations per second, cannot. To compete with advanced CMOS accelerators, operations must reach into the femtojoule regime. Research at Stanford demonstrated silicon modulators achieving sub-femtojoule energy consumption, while Nature Photonics reported femtofarad-scale optoelectronic integration with femtojoule-per-bit energy under controlled laboratory conditions. Achieving this in practical systems demands extreme photonic efficiency – tight optical confinement, minimized drive capacitance, low-loss integration – and a willingness to trade off long-term stability for raw speed and efficiency.

From Temperature Stability To Dynamic Reconfigurability

Telecom systems prize stability above all. Dense wavelength-division multiplexing depends on channels spaced by 50 GHz to 100 GHz, as defined by the ITU-T G.694.1 standard, across thousands of kilometers, according to ITU. A 10 pm wavelength drift in a laser or filter can cause catastrophic channel interference. This drove decades of engineering effort to achieve temperature-compensated lasers, athermal arrayed-waveguide gratings, and thermally stabilized silicon photonic packaging.

Computing photonics, in contrast, prioritizes agility rather than rigidity. A photonic processor need not maintain a fixed grid of ITU channels: it must reconfigure optical weights, phases, and pathways at electronic speeds. Here, wavelength drift becomes secondary; dynamic reconfigurability dominates design thinking. A microring resonator whose resonance shifts with temperature may be seen as unstable in telecom, but in computing, that same thermal sensitivity allows rapid, controllable tuning. With milliwatts of heater power, reconfiguration becomes both practical and useful.

This philosophical flip enables entirely new material strategies. Silicon’s thermo-optic coefficient of approximately 1.86 × 10⁻⁴ K⁻¹ at 1550 nm – long a nuisance for telecom – becomes advantageous, according to arXiv. Strong temperature sensitivity allows fast optical weight updates or tunable phase control. Phase-change materials such as Ge₂Sb₂Te₅ (GST), which transitions between amorphous and crystalline states, once avoided for thermal instability, are now embraced as nonvolatile optical memories and reconfigurable switches, according to ACS Publications. Nanosecond pulses can toggle GST states, providing a stable, multilevel optical weight unavailable with traditional interferometers.

Further afield, researchers are revisiting materials like liquid crystals, electro-optic polymers, and chalcogenide glasses. Their long-term drift and environmental sensitivity once disqualified them from field-deployed telecom hardware. Now, in computing environments where duty cycles last milliseconds and chips operate in tightly regulated thermal conditions, those same properties offer valuable tunability and responsiveness.

Embracing Optical Nonlinearity

Perhaps the most illuminating fault line between the two domains lies in their treatment of optical nonlinearity.

Telecommunication engineers have long viewed nonlinear effects as adversaries. Phenomena like four-wave mixing, self-phase modulation, and cross-phase modulation in optical fibers can corrupt multi-channel signals and degrade signal-to-noise ratio. Enormous effort has gone into suppressing optical intensity, broadening mode areas, and maintaining linear transmission regimes.

Photonic computing, by contrast, depends on nonlinearity as the foundation for computation. Logic gates require thresholds; neural networks depend on activation functions; adaptive optical memories hinge on hysteresis. These inherently nonlinear functions cannot emerge in purely linear photonic systems.

This inversion redefines device and material choice. Silicon’s two-photon absorption, long a loss mechanism, is now explored as a form of saturable absorption in hybrid opto-electronic architectures, though most photonic neural networks rely on electro-optic nonlinearities or opto-electronic feedback loops rather than purely optical TPA due to loss and signal-to-noise considerations. Materials like gallium arsenide and aluminum gallium arsenide, with nonlinearities orders of magnitude stronger than silica, enable femtosecond-scale all-optical switching in experimental demonstrations under resonant conditions, typically requiring milliwatt-level power. Atomically thin materials like graphene and transition-metal dichalcogenides introduce electrical tunability, where carrier density modulation alters absorption or refractive index on picosecond timescales.

Historically experimental materials – photorefractive oxides, saturable absorbers, and even plasmonic composites – are being engineered into robust nonlinear computing primitives. Their optical bistability and self-feedback effects are essential ingredients for neuromorphic and reservoir computing architectures.

Consequently, optical waveguides and resonators are being redesigned to amplify rather than suppress intensity. Computing photonics favors nanoscale confinement, high quality factors, and resonance engineering to achieve strong nonlinear interactions at minimal energies. In effect, the same light-matter interactions telecom designers spent decades suppressing now become the computational pathways themselves.

Device-Level And Architectural Implications

These divergent priorities cascade upward into architecture.

Modulators: Telecom modulators emphasize linearity, low chirp, and temperature stability across wide bandwidths. Computing modulators sacrifice some of that stability in exchange for femtojoule-per-bit efficiency in narrow-band operation. Free-carrier modulation, even with absorption penalties, remains attractive because it minimizes capacitance and drive voltage.

Detectors: In long-haul links, sensitivity and noise performance dominate; signals are weak and amplified repeatedly. In a photonic processor, detectors often sit micrometers from local amplifiers. Lower responsivity is acceptable if it enables larger arrays and reduced crosstalk.

Sources: Telecom demands coherence and thermal stability: distributed-feedback lasers with sub-100 kHz linewidths. Computing relaxes those requirements, tolerating gigahertz linewidths if total power and spectral coverage are sufficient. Frequency combs, once exotic, are now highly attractive, providing hundreds of precise wavelength channels for massively parallel weighting operations.

Interconnects: Telecom topologies favor dedicated point-to-point links and switching fabrics optimized for determinism. Photonic computing demands mesh or crossbar architectures capable of collective operations, adaptive rerouting, and configurability. This shift pushes optical network-on-chip research toward broadcast-and-weight and multicast topologies.

The Expanding Materials Palette

Underlying these architectural contrasts is an explosion in material diversity. The demands of computing systems extend well beyond silicon and silica, embracing a wider spectrum of optical functionalities.

Silicon nitride offers low loss and moderate nonlinearity for passive routing. According to Nature, lithium niobate provides a Pockels coefficient of approximately 30 pm/V, enabling sub-femtojoule switching. Gallium arsenide and other III-V semiconductors support both gain and ultrafast nonlinear interactions. Organic-inorganic hybrids enable tunable and reconfigurable photonics through electro-optic polymers combined with CMOS-compatible backplanes.

Heterogeneous integration – through wafer bonding, transfer printing, or epitaxial regrowth – is becoming a necessity rather than an academic exercise. Computing photonics demands co-location of active and passive components, thermal tuners, and electronic control circuits at densities telecom design has never required. Similarly, research into metamaterials and epsilon-near-zero structures now targets engineered nonlinear responses customized for optical logic rather than passive transmission.

System-Level Trade-Offs

At the system level, the difference between transmitting and computing with light becomes even more pronounced. Telecommunications systems must endure decades-long deployment under uncontrolled conditions. Reliability, margin, and standardization define success. Devices are qualified across temperature extremes and built for 25-year lifetimes.

Photonic computing operates under very different constraints. Devices are designed for controlled datacenter environments, replaced or upgraded on 3–5-year cycles, and evaluated primarily by performance-per-watt. Aggressive co-optimization between photonics and electronics takes precedence over long-term conservatism. Even error tolerance is redefined: where telecom strives for bit-error rates below 10⁻¹⁵, computing systems may accept higher physical-layer error rates when algorithmic redundancy or machine learning frameworks can compensate, though error tolerance remains highly application-dependent. Many scientific computing and control workloads still require precision comparable to telecommunications. The system trades precision for energy efficiency and speed in contexts where applications permit this tradeoff.

Convergence And Outlook

Yet, the two fields are not wholly divorced. Some of the most transformative computing architectures borrow tools pioneered for telecom, coherent detection, frequency comb generation, and large-scale photonic integration. Conversely, innovations in photonic packaging, control, and hybrid material integration emerging from computing research may circle back into next-generation coherent transceivers and optical interconnects.

Still, the fundamental divergence remains clear. Telecommunications photonics is about the preservation of information, faithfully conveying bits across the hostile, noisy channel of global fiber infrastructure. Photonic computing, in contrast, concerns the transformation of information, harnessing light-matter interactions to perform operations previously reserved for electrons. The optimization axes – distance versus latency, stability versus reconfigurability, linearity versus nonlinearity – could not be more different.

As photonic computing matures, it will establish its own engineering canon, just as electronics once split from radio. What began as a shared foundation of optical physics is now diverging into two distinct technological lineages: one optimized for transmitting light, the other for thinking with it.