PCB Embedded Capacitance Empowers PDN Optimization for High-Power AI Accelerators

Large model training and high-performance inference scenarios are driving iterative evolution of AI accelerator architectures, with hardware form factors rapidly shifting from traditional single-chip solutions toward multi-Chiplet heterogeneous packaging. Next-generation AI accelerator cards commonly exhibit characteristics such as high power density, large transient currents, dense high-speed SerDes links, and severe board-level space constraints. Under architectures integrating advanced packaging, high-layer-count thick PCBs, multi-rail power delivery, and high-density interconnects, the primary bottleneck limiting AI system performance is no longer raw compute capability but rather the integrity and transient stability of the Power Delivery Network (PDN).

This article focuses on the core PDN bottlenecks in AI accelerator boards, systematically analyzing the background, architectural innovations, technical mechanisms, and manufacturing barriers of PCB-embedded capacitor block technology. It clarifies the engineering value of this approach in high-power Chiplet systems and, combined with the current state of China’s industrial chain, outlines strategic technical deployments and implementation pathways across key segments to support the domestic development and process upgrading of high-end AI hardware.

I. Core Power Delivery Challenges in Next-Generation AI Accelerators

Current training-grade AI accelerators employ multi-die advanced packaging, integrating compute dies, HBM memory dies, I/O dies, and interconnect bridge structures. These systems feature multiple power rails, high-bandwidth interconnects, and highly dynamic current loads, requiring integrated layout of VRMs, passive components, high-speed interfaces, and thermal management within limited board area. In this context, the PCB is no longer merely a signal carrier but a critical component determining system power stability and overall performance.

High-power compute dies are typically concentrated at the center of the package, surrounded and shielded by peripheral functional dies. This leads to constrained power delivery paths for central dies, elongated lateral routing, increased parasitic resistance, and saturated via resources—resulting in exacerbated local voltage droop (IR Drop) and sluggish transient response. This contradiction fundamentally stems from a structural mismatch between package-level power distribution and traditional board-level power topologies, representing an inherent limitation that conventional PCB power delivery schemes cannot overcome.

II. Limitations of Conventional Backside Decoupling Solutions

Conventional high-performance boards typically adopt a power delivery scheme using “backside surface-mount decoupling capacitors connected via through-vias to BGA pads.” While adequate for medium-to-low power applications with modest transient fluctuations, this approach fails to meet the extreme demands of high-end AI accelerators. Its core limitations manifest in three aspects.

1. Parasitic Inductance Surge Due to High-Layer-Count Thick Boards

To accommodate massive high-speed differential signals and multi-layer power/ground planes, AI accelerator PCBs often use ultra-high-spec designs with over 40 layers and thicknesses around 5mm. When relying on backside capacitors for decoupling in such thick boards, current loop paths are significantly extended, causing a substantial increase in the equivalent series inductance (ESL) of the power network. This degrades high-frequency transient current compensation capability, making it incompatible with the rapid load transitions of AI cores and leading to voltage droop, high-frequency ringing, and other power integrity issues.

2. Exhaustion of Board Real Estate Renders Component Stacking Ineffective

AI boards must integrate VRM modules, high-speed connectors, optical modules, control circuitry, and mechanical reinforcement for thermal management, leaving extremely tight board space. Traditional optimization strategies that increase backside capacitor arrays to enhance power stability are physically constrained and unsustainable; the path of component stacking has reached its practical limit.

3. Custom Power Modules Increase System Cost and Mass Production Risk

To address insufficient board-level decoupling, some designs adopt customized load-side power modules to compensate for PDN weaknesses. However, this approach prolongs development cycles, increases validation complexity, reduces BOM commonality and supply chain flexibility, and significantly raises assembly difficulty and mass production risk.

III. Core Architectural Innovation of Embedded Capacitor Block Technology

PCB-embedded capacitor block technology is not merely an optimization of component placement but represents a structural reconfiguration of the board-level PDN architecture. Leveraging sequential lamination processes, this technology embeds capacitor blocks directly beneath the die shadow region—immediately below the top-layer BGA pads—effectively moving decoupling units closer to the load.

This design physically shortens transient current loops, reduces parasitic parameters, resolves power congestion for central dies, and fully liberates both top and bottom board surfaces—providing ample margin for high-speed signal routing and thermal structure optimization, thus meeting the demands of high-density, high-power AI board designs.

IV. Dual-Path Power Architecture: Layered Adaptation to Steady-State and Transient Conditions

The dual-path (upper and lower) collaborative power delivery is the core innovation of the embedded capacitor solution. By functionally separating high/low-frequency and transient/steady-state power paths, it achieves full-condition power optimization—a typical implementation of advanced layered PDN design philosophy.

1. Upper Near-End Path: Low ESL for High-Frequency Transient Loads

During AI core computation, logic units, matrix engines, and cache arrays switch synchronously at high speed, generating significant di/dt current surges. The short, proximate conductive path above the capacitor block minimizes high-frequency current loops and parasitic inductance, enabling rapid charge delivery during instantaneous load transitions. This effectively suppresses voltage undershoot and high-frequency ringing, enhances high-frequency decoupling efficiency, and resolves the slow transient response of conventional solutions.

2. Lower Far-End Path: Low-Resistance Channel for Steady-State Power Delivery

For the sustained high-current steady-state operation typical of AI systems, the vertical channel below the capacitor block offers low DC resistance, serving as the primary power delivery path to continuously supply stable energy. This effectively reduces IR drop, minimizes conduction heating, and expands VRM regulation margin, improving overall power efficiency. The architecture achieves near-end suppression of transient spikes and far-end assurance of steady-state delivery through layered collaboration, comprehensively adapting to complex AI system workloads.

V. Eliminating PTH Obstruction to Enable High-Density Die Power Delivery

In multi-Chiplet packaging architectures, peripheral dies and BGA arrays consume substantial plated-through-hole (PTH) resources. Conventional backside decoupling relies heavily on numerous PTH vias for inter-layer current flow, leading to via congestion, fractured lateral power copper traces, severely narrowed power windows for central high-power dies, and drastically increased return path losses.

Embedded capacitor technology moves decoupling functionality to the top internal layer of the PCB, eliminating dependence on board-spanning PTH vias. This dramatically reduces via obstruction and interference in the power network, preserving the integrity of power and return paths around central dies, thereby fundamentally resolving power congestion caused by high-density packaging.

VI. Core of PDN Optimization: From Component Stacking to Full-Band Impedance Control

The essence of power integrity design in high-end AI boards lies in precise full-band PDN impedance control and resonance suppression, not merely increasing capacitor count. Embedded capacitor technology achieves synergistic optimization of the three core parameters—R, L, and C—through structural innovation, enabling accurate target impedance matching.

1. Target Impedance Defines Power Design Boundaries

PDN design follows the target impedance criterion: the tighter the allowable voltage ripple and the larger the load current step, the lower the required PDN target impedance. The low-voltage, high-current, and highly transient nature of AI accelerators imposes extreme demands on impedance control precision.

2. Multi-Parameter Coordination Suppresses Voltage Disturbances

Voltage disturbances caused by load transients result from resistive voltage drop, inductive undershoot, and insufficient capacitive energy storage. This technology comprehensively improves power stability by reducing DC path resistance, minimizing high-frequency loop parasitic inductance, and placing capacitors close to the load for efficient energy storage.

3. Broadening Effective Operating Bandwidth of Capacitors

Conventional remote surface-mount capacitors suffer significant high-frequency decoupling degradation due to parasitic inductance from long traces. The embedded design drastically shortens interconnect paths and reduces parasitics, enabling capacitors to maintain effective decoupling performance across the high-frequency operating bands of AI systems, thereby extending the frequency coverage of power optimization.

VII. Industrialization Barrier: Precision Sequential Lamination Process Technology

The main challenges in implementing embedded capacitor technology lie in precision manufacturing and reliability control for high-layer-count thick boards, with sequential lamination being the key mass-production hurdle. For AI-specific PCBs exceeding 40 layers and 5mm thickness, process difficulties include: high-precision component placement, flatness and stress control during multiple lamination cycles, interlayer bonding reliability, void suppression around embedded components, structural stability under thermal cycling, and full compatibility with subsequent drilling, plating, and testing processes.

Successful mass production requires synergy across three capabilities: material systems with low loss and low CTE suitable for multiple laminations; reliable embedding and verification processes for high-layer-count thick boards; and co-simulation design capabilities integrating package, PCB, and power domains.

VIII. Engineering Value: Compatibility with Standard VRMs Reduces System Development Cost

Embedded capacitor technology enhances near-load PDN performance to compensate for board-level power deficiencies, effectively reducing reliance on customized VRM modules. This architecture enables direct reuse of standardized VRM components, minimizing custom power development effort, improving supply chain commonality, shortening product iteration cycles, and significantly lowering assembly risks and board layout pressure—greatly enhancing the manufacturability of AI accelerator cards.

IX. Industry Technology Trends

The proliferation of Chiplet heterogeneous packaging is breaking disciplinary boundaries in high-end AI hardware design. Traditional siloed approaches to package, PCB, power, and signal design are no longer sufficient, giving rise to four key industry trends:

1. Near-End Migration of Decoupling Networks: Decoupling units are progressively moving from remote board locations toward BGA-proximal, intra-board, and even intra-package positions to achieve low-parasitic, layered, full-band decoupling;

2. Vertical Power Architecture: Long lateral power paths are being replaced by vertical topologies to minimize loops, power loss, and parasitic parameters;

3. SI/PI Co-Optimization: High-speed signals and power resources are deeply coupled, requiring synchronized planning of return paths, via array optimization, and reference plane integrity;

4. Multi-Domain Co-Simulation Adoption: Design validation is evolving from isolated PI/SI simulation to full-path modeling across package-PCB-VRM, time-domain transient analysis, and integrated thermo-electro-mechanical verification.

X. Domestic Full-Chain Strategic Directions

Embedded capacitor PCB technology is a critical pathway for China’s breakthrough in high-end AI hardware localization, spanning four core segments: system design, PCB manufacturing, materials/components, and EDA tools.

1. AI Board and Server Platform Companies

Move beyond simply increasing PCB layer counts and establish an integrated co-design framework spanning package, PCB, and power domains, focusing on capabilities such as multi-rail PDN frequency-band partitioning, precise target impedance optimization, DFM/DFR standards for embedded components, and electro-thermal co-design.

2. High-End PCB Manufacturers

Focus on mass production of high-layer-count thick boards, integration of embedded passive components, reliability control in multi-cycle sequential lamination, ultra-low defect inspection, and certification systems for premium boards to overcome high-end AI PCB process barriers.

3. Materials and Passive Component Suppliers

Develop domestically produced core materials such as embedded capacitors compatible with multi-lamination processes, low-stress resin substrates, and interlayer fillers, while accumulating reliability data for components and materials under extreme operating conditions.

4. EDA Simulation Tool Vendors

Develop multi-scale SI/PI co-modeling tools, automated embedded component placement optimization, target-impedance-driven capacitor configuration, and intelligent design platforms incorporating manufacturing constraints to build a localized high-end board simulation ecosystem.

XI. Key Implementation Recommendations for Engineering Deployment

To ensure smooth mass production and mitigate design/reliability risks, a systematic engineering approach must be followed:

1. Define Metrics First, Design Accordingly: Clearly specify maximum di/dt and voltage ripple tolerance, precisely allocate frequency-band responsibilities among VRM, board-embedded capacitors, and package-level decoupling, and avoid blind component stacking;

2. Establish Layered Decoupling Architecture: Implement a tiered system where “VRM handles low-frequency steady-state, board-embedded capacitors manage mid-to-high frequency compensation, and package-level decoupling suppresses ultra-high frequencies,” balancing performance across the full spectrum;

3. Prioritize Reliability Validation: Conduct thermal cycling and stress aging tests early to mitigate long-term reliability risks such as interlayer stress and CTE mismatch introduced by embedded components;

4. Implement SI/PI Co-Design: Synchronize embedded capacitor placement with high-speed signal channels and return paths to prevent power optimizations from compromising signal integrity;

5. Establish Measurement Feedback Loops: Deploy power measurement points, TDR/VNA validation structures, and thermal monitoring on prototypes to iteratively refine simulation models with empirical data, supporting mass production decisions.

XII. Industry Segment Value and Risk Analysis

This field represents a high-barrier, validation-intensive, supply-chain-dependent premium segment with significant long-term value. Core strategic areas include: high-end AI server PCB process platforms, embedded passive component manufacturing, SI/PI co-simulation EDA tools, Chiplet co-simulation services, domestic high-reliability substrate and capacitor ecosystems, and thick-board test/validation platforms. Startups can focus on lightweight entry points such as PDN simulation consulting, high-speed/power co-design services, test fixture development, embedded process integration, and DFM tool development.

Key risks include high technical barriers, long validation cycles, stringent qualification standards from leading customers, and steep yield ramp challenges from prototype to mass production—making rapid profitability unattainable without core technological expertise and resource accumulation.

XIII. Conclusion

Embedded capacitor block technology represents a disruptive reconfiguration of power delivery architecture and spatial allocation logic in high-power AI boards. Through four key innovations—decoupling migration toward the load, dual-path layered power delivery, elimination of via obstruction, and precise impedance control—it effectively addresses the power delivery bottlenecks in multi-Chiplet AI systems. Its widespread adoption marks a shift in AI hardware competition—from isolated chip compute benchmarks toward full-chain collaboration across packaging, board design, power architecture, manufacturing processes, and materials/components.

While this technology will not fully replace conventional approaches in the short term, it is poised to become the core engineering solution for high-end AI training and inference boards, offering a critical technological breakthrough for the domestic advancement of China’s high-end AI hardware industrial chain.