Brain-Inspired Optomechanical Computing

• Brain-inspired optomechanical computing is a hardware approach that uses light–sound interactions and nonlinear dynamics to physically mimic neural network operations.
• It employs mechanisms like stimulated Brillouin scattering and cavity optomechanics to achieve high-throughput matrix–vector multiplications and analog recurrent processing.
• Practical implementations demonstrate sub-nanosecond operation speeds and ultra-low energy per operation, paving the way for scalable neuromorphic systems.

Brain-inspired optomechanical computing refers to hardware and architectures exploiting optomechanical interactions—predominantly Brillouin scatterings and cavity optomechanics—to physically implement neural-network-like operations and emulate neuronal dynamics found in biological brains. By leveraging the strong nonlinearities, high dimensionality, and fading memory of light–sound interactions in photonic structures, these systems realize fundamental primitives for neural information processing, including high-throughput matrix–vector multiplications, analog recurrent dynamics, all-or-none spiking, and programmable weighting. Such approaches promise ultra-low energy and sub-nanosecond operation, aligning with the efficiency and parallelism of biological computation.

1. Optomechanical Nonlinearities as Neural Substrates

The core physical mechanisms harnessed are stimulated and spontaneous Brillouin scattering (SBS, SponBS) and cavity optomechanical effects. In SBS, counterpropagating optical fields in a waveguide or fibre exchange energy via an intermediate acoustic wave, governed by coupled-mode equations: \begin{align} \frac{\partial}{\partial t}E_p + \frac{c}{n}\frac{\partial}{\partial z}E_p + \gamma_p E_p &= -\,K\,E_s\,\varrho\,e^{-j\delta t} \ \frac{\partial}{\partial t}E_s - \frac{c}{n}\frac{\partial}{\partial z}E_s + \gamma_s E_s &= \;K^\,E_p\,\varrho^\,e^{\,j\delta t} \ \frac{\partial}{\partial t}\varrho + v_a\frac{\partial}{\partial z}\varrho + \gamma_a\varrho &= K^\,E_p\,E_s^\,e^{\,j\delta t} \end{align} where $E_p, E_s$ are pump and Stokes optical envelopes, $\varrho$ is the acoustic envelope, $K$ is the Brillouin coupling, and $\gamma_{p,s,a}$ are loss rates. The resulting three-wave coupling produces a strong, high-order nonlinearity with intrinsic memory (due to the acoustic lifetime), which is critical for neural-like computation (Phang, 2023).

In nanoscale devices, cavity optomechanics enables control of mechanical modes via radiation pressure or piezoelectric effects, described by the Hamiltonian

$$H = \hbar\omega_c a^\dagger a + \frac{p^2}{2m} + \frac{1}{2}m\Omega_m^2 x^2 - \hbar g_0 a^\dagger a x$$

where $a$ is the optical field operator, $x$ the mechanical displacement, $\Omega_m$ the mechanical resonance, and $g_0$ the vacuum optomechanical coupling (Beltramo et al., 17 Jan 2026). This generates excitable spiking dynamics analogous to neuronal action potentials.

2. Photonic Reservoir Computing via Brillouin Scattering

Photonic reservoir computing architectures exploit SBS as a nonlinear dynamical kernel. The principal workflow consists of:

• Input encoding: Data streams modulate the amplitude of a pump laser using a Mach–Zehnder modulator, with additional masking by a pseudo-random binary sequence to map each symbol into $N_x$ time slices, achieving a high-dimensional projection.
• Passive kernel: Amplitude-modulated pump and a continuous Stokes field counterpropagate through fibre (typ. $L\sim 200\,\mathrm{m}$), interacting via SBS without active amplification or inline electronics.
• Readout: A photodiode samples the transmitted pump intensity, partitioned into $N_x$ “virtual neurons” by temporal demultiplexing. Final output computation is handled by a single Tikhonov-regularized linear regression (ridge regression), without any nonlinear training inside the optical kernel.

This system’s computational kernel is fundamentally a high-dimensional, strongly nonlinear, fading-memory dynamical system. The number of virtual nodes $N_x$ (e.g., $N_x=50$) can be increased by finer time slicing or frequency/wavelength multiplexing. The nonlinearity stems from three-way light–sound mixing, while memory is set by the acoustic phonon lifetime (typically matched to the symbol–mask period) (Phang, 2023).

The table below summarizes parameter dependencies and performance benchmarks from (Phang, 2023):

Task	Benchmark result	Comparison
Linear memory capacity ($C_{lin}$)	21.9	EO–PhRC: 31.9, AO–PhRC: 20.8
Quadratic capacity ($C_{quad}$)	3.1	EO–PhRC: 4.2, AO–PhRC: 4.0
Cross-term capacity ($C_{cross}$)	27.2	EO–PhRC: 27.3, AO–PhRC: 8.1
Total capacity	48.6	EO–PhRC: 63.4, AO–PhRC: 33.1
NARMA-10 prediction (NMSE)	0.13 at $\tau \sim 240\mu$s	0.17 ([Paquot 2012]), 0.23 ([Duport 2016])
Symbol Error Rate (SER), Channel Eq.	$10^{-2}$ up to SNR 32 dB	Parity with best optoelectronic RC

Optimal operation is found $\sim10\%$ into the multi-state side of an SBS-induced bifurcation line, maximizing computational richness. Integrated implementations promise sub-nanosecond speed and ultra-low energy operation (Phang, 2023).

3. Matrix–Vector Multiplication in Optomechanical Coprocessors

Spontaneous Brillouin scattering in arrays of coupled ring resonators enables hardware-efficient, analog matrix–vector multiplication (MVM), a fundamental neural network operation (Vovchenko et al., 17 Dec 2025). The system operates as follows:

• Architecture: Each high-Q ring supports anti-Stokes (and Stokes) sidebands and a reservoir of thermal phonons. Rings are coupled to one or more bus waveguides with tunable coupling rates $\Omega_j^{(i)}$.
• Input encoding: The thermal phonon occupation $n_j(\omega_\varphi, T_j)$ in each ring represents the input vector $x_j$.
• Weighted summation: A strong pump at frequency $\omega_0$ excites spontaneous anti-Stokes scattering, creating a photon occupancy in each ring's anti-Stokes mode proportionally to $n_j$. These modes couple into the output waveguides, where the output $y_i$ encodes a weighted sum: $$\overline{I_{a,i}} \simeq \frac{\sum_j \Omega_j^{(i)2} n_j}{\sum_k \Omega_k^{(i)2}}$$ in the strong-coupling limit. This implements $y = A x$, with weights set by the coupling strengths.
• Parallelism and scaling: Spectral multiplexing of sidebands allows up to $K_{max} \sim 10^2\!-\!10^4$ frequency channels per ring. Operation speed is set by the slowest decay rate and can reach $1\!-\!10$ ns per MVM operation, independent of $N$ (matrix size). Energy per multiply-accumulate (MAC) operation approaches $10^{-19}$ J (one pump photon per MAC), with practical system energies in the fJ–pJ range (Vovchenko et al., 17 Dec 2025).

Simulation results confirm that $N=50$ rings converge to the theoretical MVM value within $<10$ ns for random weight assignments. System noise is dominated by thermal phonon fluctuations and optical shot noise, with relative error scaling as $1/\sqrt{n_{eff}}$.

4. Integrated Spiking Neurons and Dynamic Excitability

Optomechanical spiking neurons realize SNIC (saddle-node on invariant circle)-type excitable dynamics directly in nanofabricated GaP-on-SOI nanobeams (Beltramo et al., 17 Jan 2026). Key features:

• Device structure: A GaP membrane forms a photonic crystal nanobeam supporting an optical whispering-gallery mode ($\lambda_0 \approx 1550.5$ nm, $Q_0 \sim 4.4 \times 10^3$) and a 3.078 GHz mechanical mode ($Q_m \sim 1550$). Electrodes drive the mechanical mode via piezoelectricity, and the device integrates on standard SOI photonics with a $\sim 10\,\mu\mathrm{m}^2$ footprint.
• Operating principle: In the locked state, a fast optical pulse shifts the optical detuning, imparting a phase “kick.” For sub-threshold perturbations, the system relaxes without spiking; for super-threshold kicks, the phase winds by $2\pi$ (all-or-none spike).
• Analytical threshold: The excitable threshold is given by

$$\phi_{th} = 2\,\arccos\left(\frac{\Delta_{eff}}{A}\right)$$

where $\Delta_{eff} = \Delta_0 - g_0 x_*$ and $A \propto \sqrt{P_b}$. Refractory period $T_{ref}$ is calculated via the unstable manifold integration and is measured as $T_{ref} \approx 30\,\mu$s.

• Experimental performance: Determined parameters include spike threshold ($P_{pulse}/P_b \approx 0.4$ at 6 kHz detuning), integration window ($\sim10\,\mu$s), minimum spike latency ($\sim15\,\mu$s), and energy per spike ($\sim23$ nJ). Full CMOS compatibility is achieved.

Programmable optical synaptic weights are possible using Mach–Zehnder or microheater tuning, with electromechanical gain (i.e., excitability threshold) controlled by DC bias. Prospects for long-term plasticity include phase-change or strain-engineered adjustments. All-optical output and picosecond-scale latency suggest the platform's suitability for event-driven, on-chip computation with strong parallels to biological spiking (Beltramo et al., 17 Jan 2026).

5. Brain-Inspired Architectural Features and System Implications

Optomechanical neural systems mirror several core features of brain computation:

• High-dimensional, recurrent projection: Time-multiplexing and SBS dynamics expand 1D inputs into $N_x \gg 1$ inter-correlated “virtual neurons,” mimicking dense recurrent microcircuits (Phang, 2023).
• Fading memory: The natural timescales of optomechanical interactions (acoustic lifetime in SBS, cavity decay, mechanical $Q$) provide tunable, fading memory traces, analogous to the temporal integration windows in neural tissue.
• Summation and parallelism: In analog coprocessors based on ring resonators, simultaneous summation of weighted phononic occupancies matches dendritic integration of synaptic currents. Frequency-multiplexed readout reflects biological parallelism (Vovchenko et al., 17 Dec 2025).
• Spiking and plasticity: Excitable nanobeam systems deliver all-or-none spiking, phase-dependent thresholding, temporal summation, and refractory periods in close analogy with cortical neurons (Beltramo et al., 17 Jan 2026).
• Energy efficiency and scaling: Physical constraints yield energies per operation down to $10^{-19}$ J (coprocessors) and spike energies below 25 nJ (neuron devices). Length scaling ranges from $\sim$cm fibre reservoirs to $\sim 10\,\mu\mathrm{m}^2$ monolithic neurons. No active on-chip amplification is required in purely passive kernels (Phang, 2023, Vovchenko et al., 17 Dec 2025, Beltramo et al., 17 Jan 2026).

6. Practical Considerations, Limitations, and Outlook

While these platforms demonstrate strong performance in key neural benchmarks and energy–delay metrics, practical deployment entails several nontrivial engineering challenges:

• Fabrication uniformity: Resonator arrays require consistent high $Q$-factors, phonon lifetimes, and coupling coefficients across $N$ devices (Vovchenko et al., 17 Dec 2025).
• Thermal and optical noise: Output signal-to-noise ratios are limited by phonon number fluctuations and optical shot noise; SNRs improve with higher occupation and pump powers, but are ultimately subject to trade-offs with heating and crosstalk.
• Integration and interconnects: Optical and RF routing for large-scale neuromorphic graphs demands low-loss, low-crosstalk photonic networks and potentially on-chip weighting elements (interferometers, heaters).
• Programming and training: Hardware weights are determined by static (fabrication, heater) or dynamically reconfigurable (pump, bias) parameters; in situ training remains a topic of ongoing research.
• Scaling laws: Theoretical and simulated scaling extends to $10^2\!-\!10^4$ parallel channels and $N_x\sim50$ virtual nodes, but practical implementations will be limited by thermal anchoring and integration yield.

A plausible implication is that further advances in integrated photonics and optomechanical material platforms will enable ever larger, faster, and more biologically faithful neuromorphic systems. These systems are well-positioned for AI co-processor and edge-computing roles demanding low latency, high bandwidth, and energy efficiency, while preserving the dynamic richness found in neural architectures (Phang, 2023, Vovchenko et al., 17 Dec 2025, Beltramo et al., 17 Jan 2026).