Optical neuromorphic computing, by emulating the event-driven characteristics and spatiotemporal associative memory mechanisms of biological neural systems, leverages the inherent advantages of optical signals, namely, high transmission speeds, low crosstalk, and parallel processing enabled by wavelength-division multiplexing. This approach offers significant energy efficiency advantages in pattern recognition and temporal information processing tasks. Silicon-based photonic integrated chips, which benefit from mature complementary metal oxide semiconductor-compatible (CMOS-compatible) fabrication technologies, offer unique advantages for developing optoelectronic co-processing architectures and provide a highly integrated platform for advancing optical neuromorphic computing. Micro-ring resonator (MRR), as critical components in silicon photonic integrated chips, have been employed for neuromorphic photonic computing, including reservoir computing. However, silicon-integrated MRR face two major limitations. First, the mismatch between the relaxation timescales of free-carrier effects (about 10 ns) and thermo-optic effects (about 100 ns) often results in self-pulsation within the semi-steady-state operational regime. This interrupts the coherent mapping between input signals and reservoir states, thus degrading the correlation between input features and output responses and reducing computational accuracy. Second, the inherently slow thermo-optic relaxation limits the computing speed to the Mbit/s level. In contrast, perovskite materials offer ultrafast, femtosecond-level Kerr nonlinear responses, significantly enhancing the dynamic performance and the speed of integrated photonic reservoir computing systems. Their compatibility with waveguide integration makes them promising candidates for next-generation ultrafast photonic reservoir computing hardware, offering a new path to overcome current speed limitations in neuromorphic architectures.

A dynamic model based on time-domain coupled-mode theory (CMT) is developed through Eqs. 1?3 to describe the perovskite MRR. The model incorporates Kerr-induced refractive index modulation and light-intensity interactions. The model introduces dimensionless normalized parameters to address the temporal disparity between the ultrafast Kerr relaxation time (τnl~70fs) and the photon lifetime (τp~16.7ps), ensuring computational stability. Perturbation analysis is used to derive the Jacobian matrix (Eq. 4). This allows for the efficient identification of nonlinear operating regions, such as steady states and saddle points, without the need for dynamic simulations. Subsequently, a photonic reservoir computing framework (Fig. 2) is designed by leveraging the micro-ring’s Kerr-induced nonlinear dynamics and photon lifetime-dependent short-term memory for digit speech classification tasks. The input layer utilizes Lyon’s cochlear model to preprocess audio signals, decomposing them into multiple frequency channels with uniform temporal steps. These signals are then linearly mapped to optical input power levels within the nonlinear operation range of the MRR, exploiting Kerr-induced nonlinear dynamics for high-dimensional feature mapping. In a single-node simulation, a resetting mechanism is introduced between channels to return the MRR to its initial state and avoid inter-channel interference. The transient optical responses are sampled at uniform intervals to generate virtual nodes. These are concatenated across all channels into a one-dimensional feature vector and fed to the readout layer. The final classification is performed using ridge regression with L2 regularization (Eq. 5), mapping the reservoir’s spatiotemporal nonlinear states to discrete digit categories.

The nonlinear operating range of the MRR is identified via Jacobian eigenvalue analysis [Fig. 3(a)], and dynamic simulations [Fig. 3(b)] validate this prediction, which show consistent transitions between steady states under sinusoidal modulation. The critical thresholds match closely with the analytical boundaries. Combining dynamic simulations and bifurcation analysis confirms the absence of self-pulsing within the semi-stable regime, which is crucial for reliable spatiotemporal mapping in reservoir computing. The proposed perovskite MRR-based reservoir achieves 96.5% accuracy in digit classification at an ultrahigh input rate of 200 Gbit/s, outperforming silicon-based systems limited by slower thermo-optic nonlinearities. At varying input rates [Fig. 4(b)], the reservoir maintains consistent nonlinear time-domain mappings, with dynamic variations primarily governed by photon lifetime-dependent short-term memory effects. Increasing input rates causes broader oscillations in nonlinear responses and different frequency channels exhibit distinct response dynamics [Fig. 4(c)], demonstrating the system’s remapping capabilities. Notably, channels with similar signal envelopes [e.g., channel 15 in Fig. 4(c) and channel 19 in Fig. 4(b)] yield similar response patterns, reflecting strong feature extraction capabilities. Parameter analysis shows that optimal performance is achieved using four virtual nodes per channel (Fig. 5). Exceeding this number introduces redundant or interfering features (e.g., speaker-dependent variations), reducing classification accuracy by about 3 percentage points. When input rates match the inverse photon lifetime, temporal correlation extraction is optimized, improving feature separability. However, at excessively high rates (>400 Gbit/s), insufficient light-energy accumulation weakens Kerr modulation, decreasing classification accuracy by about 5 percentage points. These results highlight the dynamic interaction between virtual node density, nonlinear mapping, and short-term memory, emphasizing the need to balance these parameters in system design.

In this paper, we propose an innovative approach for ultrafast integrated photonic neuromorphic computing using perovskite MRR with pure Kerr nonlinearity, overcoming the speed bottlenecks of conventional silicon-based platforms. Theoretical modeling via coupled-mode theory and perturbation analysis offers critical insights for designing Kerr-based reservoir architectures and identifying stable operational regimes while avoiding self-pulsing. The proposed system achieves 96.5% accuracy in speech digit classification at 200 Gbit/s, validated through dynamic simulations and parameter studies. This paper establishes a framework for high-speed, energy-efficient photonic neuromorphic systems and provides insights into material-driven optimization in nonlinear photonic reservoir design.