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Yes, embedded passives can improve power integrity in AI and high-performance computing systems, but they cannot fix power delivery on their own. Package-embedded capacitors place local charge storage closer to a processor and can reduce parasitic impedance. Actual results depend on the package, converter placement, interconnects, board-level decoupling, thermal conditions and reliability requirements.
Why AI accelerators stress the power-delivery network
A power-delivery network (PDN) carries power from conversion and distribution circuitry to the active load. AI accelerators can draw very high currents and change their demand quickly. Resistance and inductance along the delivery path contribute to voltage deviation during these load changes, making impedance and transient voltage droop key design concerns.
Decoupling capacitors provide local charge storage to help manage those changes. Their usefulness at high frequencies depends in part on parasitic inductance and the distance between the capacitor and the load. Integrating capacitance into a package or substrate can shorten part of that path.
What embedded capacitors can improve
Package-embedded capacitors are one option in a broader PDN design. The IEEE Electronics Packaging Society’s Heterogeneous Integration Roadmap states that “Package-embedded capacitors have lower parasitics and improve the electrical performance at higher clock speeds (>350 MHz).” That statement describes the roadmap’s context, not a guarantee for every package or system.
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The roadmap also describes package-embedded decoupling densities of 2 µF/mm² with approximately 100-micron films, and 20 µF/mm³. These are figures for the technology described in the roadmap, not universal specifications for embedded capacitors.
A demonstrated substrate module
A 2024 IEEE ECTC paper on an integrated Package Solution (iPaS) substrate reports an impedance of 1 mΩ at 1 MHz. In the paper’s comparison, the module reduced the number of surface-mount capacitors by more than 60% while achieving almost the same voltage droop as a general module. When the surface-mount capacitor count was not reduced, the paper reports a 14 mV (10%) improvement in voltage droop. These results apply to the specific module and comparison studied.
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Deep-trench capacitance in a silicon interposer
A 2020 study of a CoWoS logic-HBM2E power-delivery system reports a deep-trench capacitor (DTC) integrated into the silicon interposer with a capacitance density of 300 nF/mm². Compared with the studied design without the DTC, the paper reports lower impedance and first voltage droop in the logic-core area, as well as lower impedance and simultaneous-switching noise in the HBM2E PHY area. This is evidence for that platform configuration, not a general result for all packages.
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Why embedded passives are not a complete fix
Embedded capacitance helps address the local storage and parasitic parts of the PDN; it does not replace the rest of the delivery system. Converter architecture and placement, current-carrying interconnects, board-level decoupling and package routing also affect delivery from the power source to the load. A package that improves local impedance still has to operate within its thermal, mechanical and manufacturing constraints.
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Vertical power delivery addresses another part of the path by moving power conversion closer to the load. An IEEE APEC 2024 paper, “Vertical Power Delivery for 1000 Amps Machine Learning ASICs,” describes a solution capable of supplying more than 1,000 A at 0.8 V. In the paper’s comparison at a 1,000 A load, its vertical solution had 70% lower I²R loss than the conventional lateral design it studied. Those results belong to the reported architecture and comparison; they are not a universal prediction for AI systems.
On-chip decoupling optimization and package placement are also studied as ways to control cascaded PDN impedance. The practical design question is therefore not whether embedded capacitors alone can solve power delivery, but how they work with conversion, interconnect and decoupling choices across the complete path.
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How to assess a power-delivery design
There is no single ranking of approaches in the cited studies. A useful comparison should evaluate the same system and operating conditions across these dimensions:
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- Transient response: Compare voltage droop and load-step behavior under the expected current changes.
- Path parasitics: Account for interconnect resistance and inductance, along with distance from the capacitor or converter to the load.
- Integration: Weigh surface-mount capacitor count, package area and capacitance density against integration constraints.
- Power conversion and routing: Assess converter efficiency and losses in the delivery path.
- Operating life: Include thermal exposure, capacitor aging and reliability qualification.
A 2024 study of a realistic high-current server system identifies decoupling-capacitor temperature and aging as factors that can affect PDN performance. That makes operating conditions and reliability relevant to qualification; it does not establish that embedded capacitors are inherently more or less reliable than every surface-mounted alternative.
Embedded capacitance is not the same as an MLCC
The IEEE roadmap includes multilayer ceramic capacitors (MLCCs) among technologies used in lower-voltage power-delivery networks, including 0.8–12 V. MLCCs are a board-level decoupling option; they are not interchangeable with custom package-embedded capacitance, deep-trench capacitors or power modules. Each occupies a different place in the design, and the choice depends on the electrical and integration requirements.
What the available results do—and do not—establish
The cited papers provide design-specific evidence that embedded capacitance can reduce impedance or voltage droop, and that a vertical delivery architecture can reduce loss in its studied comparison. They do not establish universal benefits across accelerator packages or deployed AI systems, nor do they provide a reliable industry-wide figure for adoption, cost, manufacturing yield or total-system savings. Those outcomes depend on the particular implementation and must be evaluated for the system being designed.
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