As artificial intelligence (AI) booms—driving demand in AI servers, high-performance computing (HPC), and intelligent driving—the PCB (Printed Circuit Board) industry, a cornerstone of electronics, faces unprecedented technical challenges amid opportunities. This article analyzes these challenges across four core areas: design & performance, manufacturing complexity, data security & talent, and cost pressure.​

1. Escalated Design and Performance Requirements​

AI’s demand for real-time data processing and high computing power has raised the bar for PCB transmission efficiency, stability, and heat dissipation—requiring both quantitative and qualitative technical leaps.​

1.1 High-Frequency, High-Speed Signal Transmission​

AI servers rely on ultra-fast data transfer, with interfaces like PCIe advancing from 16Gbps/lane (PCIe 4.0) to 32Gbps/lane (PCIe 5.0), and 64Gbps/lane (PCIe 6.0) upcoming. PCBs must address this via:​ Special Materials: Traditional FR-4 substrates are obsolete; low dielectric constant (Dk: 3.0-3.5 at 10GHz) and low loss factor (Df < 0.003) materials (e.g., M9 series copper-clad laminates) reduce signal attenuation.​ Precision Layout: High-speed lines need to avoid interference (e.g., differential pair length differences < 5mil/0.127mm for NVIDIA H100 GPUs), requiring AI-augmented EDA tools for 3D electromagnetic simulation.​

1.2 Intensified Heat Dissipation​

AI chips (e.g., NVIDIA A100: 400W; H200: >700W) generate extreme heat. PCBs need:​ Innovative Structures: Multi-layer thick copper foil (4oz-10oz), dense heat vias (>50/cm²), metal substrates, or integrated liquid cooling channels.​ Rigorous Thermal Testing: Pre-production simulations (e.g., ANSYS Icepak) to ensure chip contact temperatures <85°C and board temperature differences <15°C.​

1.3 Higher Layer Count and Precision​

AI PCBs now have 30-40+ layers (e.g., Meta’s MTIAT-V1: 30-36 layers) and fine line widths/spacings (10-15μm, down from 30μm). Key challenges:​ Process Control: Layer alignment errors <5μm and etching uniformity errors <10% (via LDI machines and precision etching tools).​ Reliability Tests: Thermal shock (-55°C to 125°C, 1000 cycles), thermal cycling (0°C to 100°C, 2000 cycles), and bending tests (1000x, radius 10mm).​

1. Increased Manufacturing Complexity​

AI-driven performance demands have outpaced traditional PCB processes, with advanced technologies introducing new hurdles.​

2.1 Advanced Packaging Challenges​

Heterogeneous integration (FC-BGA, CPO) requires PCBs to adapt:​ FC-BGA Compatibility: High-precision pads (diameter error <0.02mm) and board flatness (<0.1mm/m) for small-pitch (0.4-0.8mm) solder balls.​ CPO Integration: PCBs must support both electrical and optical signals, with optical component alignment <1μm and material compatibility—currently mastered by only a few manufacturers (yield <70%).​

2.2 Elevated Process Precision​

Drilling: Microvias (0.1-0.2mm diameter) need positioning accuracy <0.01mm (via high-speed laser drills: 100,000 holes/hour).​ Electroplating: Copper layer uniformity <10% across layers, with precise adjustments for multi-layer PCBs to avoid via underplating or surface overplating.​

1. Data Security and Talent Gaps​

AI-PCB integration introduces non-technical but critical challenges.​

3.1 Data Security Risks​

AI relies on sensitive data (designs, process parameters, production metrics), risking:​ Cloud Training Leaks: Data uploaded to cloud AI platforms may be intercepted; trained models can leak info via inversion attacks.​ Supply Chain Vulnerabilities: Shared data (e.g., process parameters with suppliers) may be exposed due to weak partner security.​

3.2 Talent Shortages​

Compound Talent Gap: PCB engineers lack AI skills (machine learning, big data), while AI technicians lack PCB expertise—hindering collaboration.​ Skill Updating Pressure: Existing engineers must master AI-EDA tools and data analysis, but tech evolves faster than training.​

1. Mounting Cost Pressure​

Addressing AI challenges requires heavy investment:​ R&D Costs: New materials (e.g., low-loss laminates) take 2-3 years and >10M yuan to develop; AI design software needs long-term personnel/equipment investment.​ Equipment Upgrades: High-precision tools (laser drills: >10M yuan; LDI machines: >5M yuan) cost hundreds of millions for medium-sized enterprises—smaller firms risk obsolescence.​

AI has pushed the PCB industry into a transformative phase, with challenges in design, manufacturing, security, talent, and cost. To thrive, enterprises must boost R&D, build compound teams, and strengthen data security; governments and associations should offer policy support. Overcoming these hurdles will enable PCBs to power AI growth while achieving their own advancement.