Challenges with PCB Design in the Era of AI and Its Solution

The complexity of embedded systems and hardware is rising quickly due to advances in machine learning and artificial intelligence. For those responsible for creating boards for these intricate systems, printed circuit board (PCB) designers now face additional difficulties. Here will examine some of the major issues that hardware PCB designers are facing in the AI era in this blog article and talk about possible solutions.
Growth of Multilayer Boards
The complexity of PCB designs is rising as a result of more multilayer boards, which is one of the main issues. The increasing integration of FPGAs, GPUs, specialized AI chips, and high-speed connections in AI/ML systems makes routing all these signals on a 2-layer board more challenging. Multilayer boards with 8, 12, or even more layers have become more common as a result of this. Challenges like higher design/validation cycles, concerns about thermal management, and problems with signal integrity arise when routing dense systems across multiple layers. Strong EDA tools that can handle intricate multilayer routing with design rule verification and signal integrity analysis are essential for PCB designers.
Growth of Interfaces at High Speeds
High-speed connections like PCIe, CXL, and CCIX are required when AI/ML workloads shift to edge devices to speed up model inference. It can be difficult to ensure impedance matching, reduce crosstalk, and manage skew when routing high-speed differential pairs across a multilayer board. Any infraction may result in problems with signal integrity, such as jitter. To detect and address such issues, PCB designers require simulation tools that are capable of doing pre-analysis on high-speed channel designs. It could be necessary to use techniques like pre-emphasis/de-emphasis and controlled impedance routing.
Challenges in Thermal Management
PCB designers are also facing difficulties with thermal management due to the rising transistor density and power consumption of AI devices. Because high-power AI accelerators can produce more than 100 W of heat, it’s essential to keep them working at safe temperatures to guarantee dependability. For this, the PCB must be carefully heat modelled during the design stage. It is necessary to use methods like heat spreaders, thermal vias, thermal planes, and enough airflow. The early analysis of temperature profiles and hotspots can be aided by the integration of thermal simulation tools with the PCB design processes.
Growth of Integrated Circuits
Artificial intelligence (AI) is being used by edge and Internet of Things (IoT) devices for tasks like speech recognition, computer vision, and predictive maintenance. As a result, designing embedded hardware utilising FPGAs, MCUs, specialised AI chips, sensors, and other peripherals are now more sophisticated. PCB designs must effectively package these many components while taking form factor, power delivery, signal routing, and thermal/mechanical needs into account. To meet demands for density and miniaturisation, 3D multi-chip module (MCM) packaging solutions are becoming more and more popular.
Reduced Design Cycles
There is pressure to launch new features and products quickly because of the rapid pace of AI advancement. This results in extremely shortened cycles for product development. It is expected of PCB designers to complete intricate, excellent designs within extremely constrained timeframes. Even though productivity has increased thanks to sophisticated EDA tools, it is still difficult to confirm accuracy and finish validation/compliance testing in short cycles. To some extent, this can be addressed by methodologies like virtual prototyping and hardware-software co-design.
Controlling Complexity in Design
The increasing integration of hardware functionality by AI/ML systems is posing a challenge in controlling the intricate PCB designs. The capabilities of current EDA tools are pushed to their limits by large multi-layer boards with thousands of nets, high-speed connectors, and thermal constraints. At this magnitude, tasks like documentation, regression testing, and change management become challenging. To manage complexity, design teams must implement techniques like automated validation routines, centralised databases, and modular/hierarchical design.
Answers to PCB Design Problems
Despite the considerable obstacles, PCB designers and the EDA sector have developed creative ways to overcome them:
Advanced EDA Tools: Such tools are thermal modelling instruments, virtual prototypes, high-speed channel analysis, and high-capacity routing. Potentially, the software must provide all automation, change management, and team-based design.
Methodologies: Usage of such industry best practices as requirements management, automated verification procedures, hardware-software co-design, DFM/DFT, and modular design.
Standards: Signal integrity is enhanced through proscribing to high-speed interface standards including PCIe and CXL. Reliability is maintained by thermal constraints.
Materials: And thermally conductive and high-frequency materials assist in heat dissipation and routing. Thick organic substrates with multiple layers allowed thickness.
Packaging Technologies: Miniaturisation is aided by 3D/MCM packaging, sophisticated ball grid arrays, and embedded passives and actives.
Prototyping: Hardware-assisted verification and FPGA-based emulation minimise validation cycles through fast prototyping.
Automation: Tools that facilitate automated signal integrity analysis, thermal simulation, test vector creation, and design rule/LVS checking expedite validation.
Collaboration: Geographically dispersed teamwork is facilitated by centralised databases and collaboration technologies.
Training: Providing designers with advanced techniques such as system-level thinking and hardware-software co-design.
New Technologies
Future PCB design issues should also be addressed by several developing technologies:
Machine learning (ML) is being used in EDA to automate repetitive activities, enhance performance, and find the best solutions. Examples of these jobs include routing, DRC, signal integrity analysis, and thermal modeling. This can assist in handling incredibly complicated and huge AI hardware designs.
In-Memory Computing: Non-volatile memory and resistive RAM are two examples of technologies that enable computation to take place inside memory, removing obstacles to data transfer. New PCB architectures tailored for AI workloads will be made possible by this.
The ability to become a top PCB design firm over the years has been made possible by the experience we’ve accumulated. They now offer engineering solutions to Tier 1 clients in a variety of market categories. A crucial step in the hardware development process is PCB design, and They provide high-caliber PCB hardware design services. Schematic capture, library development, package engineering, mechanical design, PCB layout, and hardware manufacturing are some of their integrated solutions.
Conclusion:
In conclusion, the growing complexity, the need for multidisciplinary needs, and the short design cycles of AI/ML hardware present special problems for PCB design. To effectively address these difficulties, designers might use collaborative workflows, training, improved EDA tools, design processes, standards, materials, and packaging technologies. The EDA sector and hardware teams are well-positioned to support the quick speed of AI development with dependable, high-quality PCB designs thanks to ongoing innovation.
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