---
title: Electronic-Photonic Design Automation (EPDA)
url: https://www.emergentmind.com/topics/electronic-photonic-design-automation-epda
type: topic
---

# Electronic-Photonic Design Automation (EPDA)

Electronic-Photonic Design Automation (EPDA) denotes a **full-lifecycle, cross-layer design automation stack** for electronic-photonic integrated circuits and photonic AI systems, spanning **device-/circuit-level simulation**, **architecture-level modeling**, **component inverse design**, **chip/layout automation**, and **closed-loop co-optimization across these layers** [2604.10841]. In parallel, it is framed as the automation layer needed to make electronic-photonic integrated circuits practical, scalable, and sustainable, because manual flows do not scale to systems whose behavior depends simultaneously on device physics, circuit topology, routing, memory, dataflow, manufacturability, and calibration [2509.07396].

## 1. Historical emergence and scope

An early photonic design automation tool embedded photonic layout generation directly into **Cadence/Virtuoso** through **SKILL**, enabling **seamless integration of photonic and electronic design in a single environment** [1504.03669]. That tool addressed **advanced CMOS nodes** such as **45 nm SOI**, where photonic devices had to coexist with large-scale electronic circuits while satisfying **10,000 to 50,000 design rules**. It introduced **technology-independent “photonic” design levels**, custom **waveguide ports**, **auto-routing of waveguide connections**, and a system-level flow in which DRC cleanup occurs **after placement**, not merely at isolated device level [1504.03669].

This early stage already established several durable EPDA themes. First, photonic design could not be reduced to ordinary electronic layout editing, because photonic objects carry optical intent, port orientation, and process-specific exclusion semantics. Second, manufacturability had to be automated rather than repaired manually. Third, electronic-photonic co-design required a shared CAD environment rather than disconnected optical and electrical tools [1504.03669].

Later literature generalized EPDA from a layout-centric capability into a broad systems discipline. The field now treats EPDA as the missing infrastructure connecting simulation, inverse design, architecture modeling, physical implementation, and back-annotation, with the explicit goal of moving from laboratory prototypes to a **scalable, reproducible electronic-photonic ecosystem** [2604.10841]. A notable correction to a common misconception follows from this progression: EPDA is not only a photonic analogue of digital place-and-route; it is a cross-layer methodology whose targets include manufacturability, calibration, workload mapping, and system-level realism.

## 2. Core abstractions and modeling layers

A defining feature of EPDA is the replacement of ad hoc photonic scripting with explicit machine-readable representations. In **SimPhony**, the central abstraction is a **hierarchical, netlist-based representation of photonic circuits** using **directed 2-pin nets** rather than undirected multi-pin electrical nets, because optical signal flow is directional. A minimal functional block is defined as a **node**, larger circuits are assembled hierarchically, and the result is a **weighted directed acyclic graph (DAG)** used for **critical-path extraction, insertion-loss propagation, link-budget analysis, and layout-aware area estimation** [2411.13715]. This representation supports both dynamic array-style architectures such as **TeMPO** and static mesh-style architectures such as **Clements-style MZI meshes** [2411.13715].

At the physical-layout layer, **LiDAR 2.0** introduces a **YAML-based PIC intermediate representation**, explicitly inspired by **LEF/DEF**, to capture **photonic components, connectivity, hierarchy, and module reuse** [2505.17239]. In agentic design workflows, **PhIDO** uses a **YAML-based DSL** compatible with **GDSFactory**, where components are encoded as **nodes**, interconnects as **edges**, and the representation also stores component parameters, PDK selection, wavelength band, port types, and metadata [2508.14123]. This suggests that EPDA is converging on formal interchange layers that decouple specification, schematic intent, and physical realization.

Compact-modeling and co-simulation form the complementary abstraction layer. A **data-driven Eigenmode Propagation Method (DEPM)** represents linear passive devices through a Hamiltonian built from intrinsic waveguide parameters extracted via complex coupled-mode theory, yielding **millisecond-scale simulations** within the model’s valid scope and enabling rapid tolerance analysis and wavelength sweeps [2501.06774]. At circuit level, electro-optic co-simulation has been moved into standard electronic simulators by expressing photonic devices as interoperable **Verilog-A compact models**. Using a baseband-equivalent optical representation, an optical link testbench was validated against experiment up to **64 Gbaud**, with models including optical losses, back-reflection, nonlinearity, high-frequency response, and noise [2410.02282]. The resulting EPDA picture is not a single tool but a stack of interoperable abstractions: graph-level circuit intent, compact device models, and physical-layout representations.

## 3. Placement, routing, and physical implementation

Modern EPDA treats photonic physical design as a first-class optimization problem rather than a schematic afterthought. In placement, **Apollo** is described as the first **GPU-accelerated, routing-informed placement framework** for large-scale PICs [2504.18813]. Its objective,
$$
\mathcal{L}(x, y) = \textrm{cosWA}(x, y) + \lambda_{NS} \cdot NS(x,y) + \mathcal{D}(x, y),
$$
combines an **asymmetric bending-aware wirelength** surrogate, a **routing-informed net spacing model**, and an electrostatic density term [2504.18813]. Across all benchmarks, Apollo reports a **geometric-mean routing success rate of 94.79%** with **48.72 s geometric-mean runtime**, compared with **50.85%** for DREAMPlace+RO and **51.38%** for Cypress [2504.18813]. The significance is straightforward: in PICs, placement quality is largely determined by whether routing will later be physically legal.

Detailed routing required a parallel rethinking. **APR** and later **LiDAR** formalize routing as geometry-aware search over curvilinear waveguides, with explicit handling of **minimum bend radius**, **port orientation**, **waveguide spacing**, **crossing legality**, and **insertion loss** [2410.01260]. Their routing objective is not merely path length. Instead,
$$
IL(p_i)=\sum_{m_j\in p_i}IL(m_j)+\sum_{n_j\in p_i}IL(n_j),
$$
with route loss decomposed into propagation, crossing, and bend terms; the global target is
$$
IL_{max} = \max_{p_i \in P} IL(p_i).
$$
APR reports **DRV-free layouts on all benchmarks**, **14% lower average critical-path insertion loss**, and **6.25× speedup** over the baseline on average [2410.01260].

**LiDAR 2.0** extends this to **hierarchical routing** with **sub-circuit reuse**, **redundant-bend elimination**, **crossing space preservation**, and routing-order refinement [2505.17239]. On spacious layouts it reports **up to 16% lower insertion loss** and **7.69× speedup** over prior methods, while on compact layouts it reports **9% lower insertion loss** and **6.95× speedup** over LiDAR 1.0 [2505.17239]. A common misconception is that photonic routing is simply “electronic routing with curves.” These results show otherwise: same-layer crossings are allowed but must be inserted physically, port access is directional, and routing quality is dominated by optical loss rather than only congestion.

For **active PICs**, physical implementation also includes metal interconnect. A planning-guided automated electrical router introduces a **physically-aware global planner**, **sequence-consistent track assignment**, and **soft guidance-assisted detailed routing** that explicitly consume previously routed waveguides and photonic keep-outs [2509.23764]. Across large active PIC designs, it reports about **99% reduction in via count**, about **98% reduction in user-specified design rule violations**, and about **17× runtime speedup** on average [2509.23764]. EPDA therefore includes both waveguide routing and photonics-aware electrical routing within one physical-design stack.

## 4. Inverse design, legalization, and yield optimization

Inverse design introduced a new EPDA problem: highly compact devices could be optimized electromagnetically yet remain difficult to fabricate or integrate. **ADEPT** addressed one part of this problem by turning photonic tensor-core topology search into a differentiable EPDA task, jointly optimizing depth, routing permutations, and coupler placement under explicit footprint constraints [2112.08703]. Across benchmarks, searched topologies achieved **competitive matrix representability**, **2-30x higher footprint compactness**, and **better noise robustness** than prior manually designed structures [2112.08703]. This established automated topology synthesis as an EPDA function rather than a manual circuit-design exercise.

At device-and-layout scale, **PoLaRIS** unifies **BOSON** and **MAPS** for fabrication-aware inverse design with **Apollo** and **LiDAR** for system-level placement and routing, targeting **DRV-free, performance-optimized layouts** for PICs with **hundreds to thousands of components** [2507.22301]. Its importance lies in coupling **physics-aware** device synthesis with **layout-rule-aware** implementation. A plausible implication is that EPDA is moving from stagewise optimization toward flows in which device synthesis and physical layout are co-designed from the outset.

Manufacturability required an additional stage beyond inverse design. **OptiClear** defines an explicit **post-design legalization stage for EPDA** for **curvilinear inverse-designed photonic devices** [2607.03632]. It operates directly on the pixelated mask using morphology:
$$
Open(M) = Dilate(Erode(M)), \qquad Close(M) = Erode(Dilate(M)).
$$
The legalizer seeks masks that satisfy the stationary condition
$$
M = Open(M) = Close(M).
$$
Two engines are provided: **OptiClear-R**, a rule-based morphological legalizer, and **OptiClear-D**, a differentiable legalizer formulated as a **minimum-distortion mask optimization problem** [2607.03632]. Across diverse inverse-designed devices and design-rule settings, both engines reduce design-rule violations from **thousands to zero**, while the differentiable version more faithfully preserves optical functionality [2607.03632]. This directly corrects another misconception: fabrication-aware inverse design alone, with soft penalties on small features and curvature, does **not guarantee** final DRC compliance.

The most comprehensive yield-native flow is **OptoSynthesizer**, which integrates **OptoSynthesizer-InvDes**, **OptoSynthesizer-Place**, and **OptoSynthesizer-Route** into an end-to-end physical design automation flow for **yield-optimized, inverse-designed EPICs** [2604.15493]. It packages robust inverse-designed devices as **PDK cells**, applies **photonics-aware inverse lithography** through **PRISM**, performs routing-informed placement and hierarchical optical/electrical routing, and then evaluates post-layout and post-fabrication behavior [2604.15493]. Reported case studies include yield improvements to nearly **90%** or higher for several inverse-designed devices, **more than 1,000 components in about 100 seconds** during placement, and **more than 8,000 nets in 425 seconds** during routing [2604.15493]. Here EPDA becomes explicitly yield-aware rather than only geometry-aware.

## 5. Cross-layer co-design, co-simulation, and calibration

A major branch of EPDA focuses on system modeling rather than only geometry synthesis. **SimPhony** is presented as a cross-layer modeling and simulation framework for heterogeneous EPIC AI systems, spanning **SimPhony-DevLib**, **SimPhony-Arch**, and **SimPhony-Sim** [2411.13715]. It supports **generic, extensible hardware topology representation**, **optics-specific dataflow modeling with multi-dimensional parallelism and reuse**, **data-aware energy modeling with realistic device responses**, **layout-aware area estimation**, **link-budget analysis from critical-path optics**, **bandwidth-adaptive memory modeling**, and **hardware/software co-simulation** [2411.13715]. One concrete result is that **layout-unaware methods can underestimate node area by 72%**, underscoring why EPDA must include layout realism even at architecture-exploration stage [2411.13715].

Open-source EPDA roadmaps extend this further. A cross-layer co-design framework organized around **SimPhony**, **NeurOLight**, **PACE**, **PIC\(^2\)O-Sim**, **MAPS**, and **SP\(^2\)RINT** argues that EPDA should be **open-source**, **AI-augmented**, **cross-layer**, and **fabrication-aware**, with eventual **push-button arch-to-layout automation** [2601.00130]. The same perspective emphasizes that electromagnetic simulation is often too slow, device design remains heuristic/manual, and hardware non-idealities are weakly coupled to AI-level metrics unless a unified automation stack is available [2601.00130].

EPDA also includes control and calibration for analog photonic hardware. In resonator weight banks, practical deployment requires automation across **device physics**, **fabrication variation**, **control electronics**, **sensing circuitry**, and **system-level inference performance** [2203.01792]. The reviewed workflow distinguishes feedforward calibration from embedded-sensor **feedback control**, models thermal crosstalk through a matrix \(\mathbf{K}\), and treats sensing and actuation choices as design variables rather than external operating assumptions [2203.01792]. This broadens the scope of EPDA beyond layout and routing: in analog photonic systems, calibration and disturbance rejection are part of the design-automation problem.

## 6. AI-generated and agentic EPDA

LLMs and agentic systems have recently been positioned as an additional EPDA layer, but the literature is careful about both capability and limitation. **PICBench** is the first open-source benchmark and evaluation framework for using LLMs to generate PIC designs, with **24 carefully designed PIC problems**, **expert-written golden solutions**, simulation-based functional checking in **SAX**, and **Pass@1**/**Pass@5** evaluation [2502.03159]. The benchmark shows that **restrictions help a lot**, **feedback helps a lot**, and that current models can often learn structural patterns of PIC netlists while still struggling with domain-specific correctness and functional validity [2502.03159]. This makes one point unambiguous: contemporary LLMs are not yet reliable one-shot PIC designers.

**PhIDO** extends the ambition from netlist generation to **natural-language photonic integrated circuit design requests into layout mask files** through a four-stage multi-agent pipeline: **Interpreter**, **Designer**, **Layout**, and **Circuit verification** [2508.14123]. Tested on **102 design descriptions** ranging from single devices to **112-component PICs**, it reports **up to 91%** success rate for single-device designs, while for queries with **less than or equal to 15 components**, **o1**, **Gemini-2.5-pro**, and **Claude Opus 4** achieve the highest end-to-end **pass@5** success rates of approximately **57%** [2508.14123]. The paper explicitly frames the endpoint as **structural validity**, not fabrication sign-off with a commercial PDK/DRC deck [2508.14123].

A more ambitious direction is **agent-driven propose → simulate → evaluate → iterate** design [2606.00915]. In one routing/layout demonstration, an agent working with **PhotonForge** reduced an initial **192 DRC violations**—including **30 heater cut-throughs** and **162 route crossings**—to **0** after **27 iterations**, with runtime **2 minutes 25 seconds**, whereas a human engineer typically needs **2–3 hours** on the same chip [2606.00915]. The same framework also demonstrated passive-device design, active-device junction exploration, RF electrode design, and an integrated silicon photonic modulator workflow [2606.00915]. Taken together, these results suggest that agentic EPDA is best viewed as orchestration over simulators, rules, and verification loops, not as a replacement for the underlying physical-design and modeling stack.

## 7. Outlook and unresolved directions

Recent reviews place EPDA at the center of a roadmap from prototypes to **scalable, reproducible electronic-photonic ecosystems**, emphasizing future needs such as **co-packaged optics**, **multi-layer / 3D photonics**, **LVS and signoff beyond geometric DRC**, and **post-layout back-annotation for system co-simulation** [2604.10841]. This suggests that current EPDA flows, while increasingly end-to-end, still stop short of the full signoff and interoperability maturity long associated with advanced electronic EDA.

Sustainability perspectives add another axis. EPDA is described as a sustainability enabler because it can **systematically optimize placement and routing solutions** to generate **more compact layouts for large-scale EPICs** and use **fewer routing layers**, thereby reducing die area, manufacturing complexity, and embodied carbon footprint [2509.07396]. In this view, compact placement and routing are not merely geometric optimizations; they are lifecycle optimizations.

Several technical gaps are already identified by the literature itself. **OptiClear** explicitly points toward **future end-to-end design-rule-aware inverse design**, where legalization and synthesis are integrated rather than treated separately [2607.03632]. **OptoSynthesizer** makes clear that broader foundry support, richer device libraries, and stronger standard benchmarks remain necessary for standardized, reproducible, yield-native EPDA [2604.15493]. A plausible implication is that the next phase of EPDA will be defined less by isolated tools than by how completely these tools share representations, back-annotate physical effects, and close the loop from specification to fabrication-ready, calibrated, and workload-validated systems.

Source: https://www.emergentmind.com/topics/electronic-photonic-design-automation-epda