PRISM: Photonics-Informed Inverse Lithography for Manufacturable Inverse-Designed Photonic Integrated Circuits

2026-02-17

AI Summary

This work addresses the challenge that inversely designed photonic integrated circuits, featuring subwavelength irregular structures, are highly sensitive to fabrication variations, often resulting in performance degradation and low yield, compounded by the absence of efficient and scalable mask optimization techniques. To overcome this, the authors propose the PRISM framework, which innovatively integrates a physics-driven differentiable fabrication model with optical-performance-oriented inverse mask optimization. This approach achieves high-fidelity manufacturing using only a small set of compact calibration patterns. Demonstrated across both electron-beam and deep-ultraviolet lithography processes, PRISM significantly enhances post-fabrication performance and yield for a variety of inverse-designed devices while substantially reducing the required calibration area and process turnaround time.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Recent advances in photonic inverse design have demonstrated the ability to automatically synthesize compact, high-performance photonic components that surpass conventional, hand-designed structures, offering a promising path toward scalable and functionality-rich photonic hardware. However, the practical deployment of inverse-designed PICs is bottlenecked by manufacturability: their irregular, subwavelength geometries are highly sensitive to fabrication variations, leading to large performance degradation, low yield, and a persistent gap between simulated optimality and fabricated performance. Unlike electronics, photonics lacks a systematic, flexible mask optimization flow. Fabrication deviations in photonic components cause large optical response drift and compounding error in cascaded circuits, while calibrating fabrication models remains costly and expertise-heavy, often requiring repeated fabrication cycles that are inaccessible to most designers. To bridge this gap, we introduce PRISM, a photonics-informed inverse lithography workflow that makes photonic mask optimization data-efficient, reliable, and optics-informed. PRISM (i) synthesizes compact, informative calibration patterns to minimize required fabrication data, (ii) trains a physics-grounded differentiable fabrication model, enabling gradient-based optimization, and (iii) performs photonics-informed inverse mask optimization that prioritizes performance-critical features beyond geometry matching. Across multiple inverse-designed components with both electron-beam lithography and deep ultra-violet photolithography processes, PRISM significantly boosts post-fabrication performance and yield while reducing calibration area and turnaround time, enabling and democratizing manufacturable and high-yield inverse-designed photonic hardware at scale.

Problem

• inverse design
• manufacturability
• photonic integrated circuits
• fabrication variation
• mask optimization

Innovation

• inverse lithography
• photonic inverse design
• differentiable fabrication model
• manufacturability
• mask optimization