Capturing design intent is critical for AI to function as a true partner in PCB design.

For the past several years, the discussion around artificial intelligence in PCB design has largely focused on capability. How fast can AI route? How many rules can it process? How well can it replicate known good designs? These are reasonable questions, but they are increasingly the wrong ones.

Ultra HDI success depends on designing within production limits, not pushing minimum capabilities.

There’s a point many design teams reach where routing density that once seemed impossible becomes routine. Then the design hits the fabrication floor, and questions begin.

Stackup choices. Mask clearances. Registration tolerances. Individually, these may seem like small details. But in ultra HDI (UHDI), small decisions compound quickly – impacting yield, cost and timelines.

Capacitors shape PDN performance by controlling impedance, supporting transient loads and stabilizing voltage across the system.

A power delivery network (PDN) is the electrical infrastructure that distributes stable, low‑noise power from voltage regulators to integrated circuits (ICs). As digital systems increase in speed and complexity, PDN design has become a critical discipline for ensuring signal and power integrity and overall system reliability. Among all PDN elements, capacitors play the most influential role in shaping impedance, supporting transient loads and suppressing noise. This article defines the PDN, outlines its objectives and major components, and examines capacitor functions in a PDN design, including bypass, decoupling and bulk capacitors. Also, it will study the guidelines for selection and placement on the PCB.

Design of Experiments can still deliver meaningful process insights by adapting proven methods to real-world manufacturing constraints.

Design of experiments (DoE) is an efficient method for planning experiments. DoE involves intentionally changing one or more input-process factors – also known as independent variables – to observe how these changes affect output or response variables (the outcomes measured in an experiment). DoE is often used for product and process design, development and improvement. To maximize information and minimize time, resources or cost, a properly chosen experimental design is needed.