Architecting Zeta-Scale Inference: A Spectral-Spatial Tensor Flow Approach on Wafer-Scale Lithium Niobate

The transition of artificial intelligence (AI) models to the zeta-parameter scale demands a computational paradigm that transcends the thermodynamic limits of resistive electronic logic. While integrated photonics offers high bandwidth, current optical neural networks (ONNs) are hindered by low analog precision and thermal instability. Here, we propose a wafer-scale, thin-film lithium niobate (TFLN) photonic processor architecture designed to break this precision bottleneck. By theoretically synergizing the linear Pockels effect of TFLN with a Spectral Residue Arithmetic (SRA) framework, we project that parallelizing 128 dense wavelength-division multiplexing (DWDM) channels can achieve an effective numerical precision equivalent to IEEE 754 single-precision (FP32). Furthermore, we design an on-chip hybrid electro-optic activation unit and a physics-aware deep reinforcement learning (DRL) thermal calibration protocol. Our Python-based behavioral simulation demonstrates that such a system could support a compute density of 1,240 TOPS/W with sub-nanosecond latency, offering a potential two-order-of-magnitude improvement in energy efficiency over state-of-the-art electronic GPUs.