arXiv:2606. 00228v1 Announce Type: new Abstract: In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 26713v1 Announce Type: new Abstract: As semiconductor technology nodes scale, computational lithography is essential for ensuring yield and performance.
arXiv:2604. 23658v2 Announce Type: replace-cross Abstract: Chip placement plays an important role in physical design.
arXiv:2603. 15925v2 Announce Type: replace Abstract: Inverse design aims to find design parameters $x$ achieving target performance $y^*$.
PICasso is an AI‑enabled framework that converts natural‑language specifications into manufacturable silicon photonic integrated circuits (PICs) through a structured pipeline of NL → YAML → GDS, PDK‑aware knowledge injection, automated placement and routing, DRC/LVS validation, and SAX‑based photonic simulation. The authors introduce PIC‑Set, a benchmark of 36 parameterized PIC design tasks, and evaluate several large language models (LLMs) using new metrics such as structural and functional Spec@k, optimization efficiency, and robustness. Across the benchmark, PICasso markedly improves specification satisfaction, achieving up to 92.7% structural Spec@3 and 52% functional Spec@3, while reducing mean insertion loss from 4.98 dB to 3.25 dB through simulation‑guided optimization.
arXiv:2606. 11247v1 Announce Type: cross Abstract: Generative models are increasingly used to propose designs, data, and control actions for physical systems, yet many such systems are governed by hard physical constraints rather than by perceptual plausibility.
arXiv:2610.01233v1 Announce Type: new Abstract: Flow matching is central to 3D generation, yet in practice its reinforcement learning (RL) methods are largely adapted from 2D visual generation. Repre...