arXiv subjects

Hyoseok Park

Publications and source records attributed to Hyoseok Park.

4 recordsLinked to original sources

arXiv2026

Information-optimized color metalenses for camera imaging

A metalens is conventionally built by prescribing a target phase and matching each meta-atom to it, and making that phase achromatic across the visible band is the main difficulty in color imaging. We remove the prescribed phase entirely. The meta-atom width map is optimized directly against the information the raw, noisy, mosaic-sampled camera measurement carries about the target image, differentiated through Maxwell propagation, the color-filter mosaic, pixel integration and sensor noise, with material, thickness and library held fixed. On a single-layer silicon-nitride platform, starting from a hyperbolic design, this raises delivered target information by 15.8% despite lower collected charge, produces the most balanced set of channel responses at the sensor plane relative to their respective axial maxima, and reduces blue-channel blur. A high-signal-to-noise held-out reconstruction improves in spatial fidelity and color accuracy. The same width-only optimization improves color across silicon-nitride, silicon-dioxide and titanium-dioxide libraries.

physics.optics↗

arXiv2026

Imaging-system-aware color routers optimized for imaging information

Conventional nanophotonic color routers are typically optimized under idealized, normal plane waves. However, this standard assumption fails in real-world imaging-system environments, where structures are illuminated by converging light cones and field-dependent chief-ray angles. Here, we present an imaging-system-aware, end-to-end inverse-design framework that directly maximizes the mutual imaging information $\Iimg$ preserved by a single-layer silicon nitride color router under realistic pupil illumination. By analytically embedding the optimal reconstruction decoder directly inside the gradient loop, we co-design the optical nanostructures and the digital recovery pipeline. To scale this approach across a full sensor, we exploit the $D_4$ symmetry of the square pixel lattice, tiling $48$ distinct sensor-field regions using only six unique lithographic masks. Our optimized router is predicted to collect $2.8\times$ more photoelectrons than a conventional color-filter array. Consequently, under low-light conditions, below a green-site signal-to-noise ratio of $13.7$~dB, the color router preserves superior image information compared to the color-filter array; evaluated from its measured routing fractions together with the modeled throughput, the fabricated device reproduces this crossover at $11.1^{+2.1}_{-2.3}$~dB. This marks the first experimental demonstration, from measured routing and a modeled throughput, of a single-layer nanophotonic color router achieving a performance crossover against the color-filter array. These results establish that next-generation flat optics must shift from isolated device efficiency toward system-level co-design optimized under physical imaging-system-pupil geometry.

physics.optics↗

arXiv2026

Photonic Exponential Approximation via Cascaded TFLN Microring Resonators toward Softmax

The rapid growth of large-scale AI models has intensified energy consumption and data-movement challenges in modern datacenters. Photonic accelerators offer a promising path by executing the linear matrix multiplications of transformer inference at high throughput and low energy. However, the softmax attention layer, which requires element-wise exponentiation followed by normalization, still relies on electronic post-processing, creating an electro-optic conversion bottleneck that negates much of the potential photonic advantage. We present a cascaded micro-ring resonator (MRR) architecture that synthesizes the per-channel exponential function required by softmax, e^{x_n - max(x)}, over a finite interval with tunable worst-case relative error. A control signal detunes each ring via an electro-optic mechanism; a weak probe at fixed frequency experiences Lorentzian transmission, and cascading N identical stages yields a multiplicative transfer function whose logarithm is approximately linear. We derive mapping rules, depth-scaling estimates, and a minimax fitting formulation, and validate the framework with three-dimensional FDTD simulations of X-cut thin-film lithium niobate (TFLN) add-drop micro-ring resonators. Direct multi-ring FDTD validation extends to a five-ring cascade and confirms agreement with theory primarily over the upper operating range; deeper cascades and higher quality factors are assessed analytically. The cascade implements the per-channel exponential block, the key missing nonlinearity for photonic softmax. We further present a WDM-parallel chip architecture with closed-loop PI feedback that completes the full softmax-exponentiation, summation, and normalization-on a single photonic chip without per-channel normalization circuitry.

physics.optics↗

arXiv2026

PRISM: Breaking the O(n) Memory Wall in Long-Context LLM Inference via O(1) Photonic Block Selection

Long-context LLM inference is bottlenecked not by compute but by the O(n) memory bandwidth cost of scanning the KV cache at every decode step -- a wall that no amount of arithmetic scaling can break. Recent photonic accelerators have demonstrated impressive throughput for dense attention computation; however, these approaches inherit the same O(n) memory scaling as electronic attention when applied to long contexts. We observe that the real leverage point is the coarse block-selection step: a memory-bound similarity search that determines which KV blocks to fetch. We identify, for the first time, that this task is structurally matched to the photonic broadcast-and-weight paradigm -- the query fans out to all candidates via passive splitting, signatures are quasi-static (matching electro-optic MRR programming), and only rank order matters (relaxing precision to 4-6 bits). Crucially, the photonic advantage grows with context length: as N increases, the electronic scan cost rises linearly while the photonic evaluation remains O(1). We instantiate this insight in PRISM (Photonic Ranking via Inner-product Similarity with Microring weights), a thin-film lithium niobate (TFLN) similarity engine. Hardware-impaired needle-in-a-haystack evaluation on Qwen2.5-7B confirms 100% accuracy from 4K through 64K tokens at k=32, with 16x traffic reduction at 64K context. PRISM achieves a four-order-of-magnitude energy advantage over GPU baselines at practical context lengths (n >= 4K).

physics.optics↗