Applications & Outlook

High Bandwidth Memory is the most consistent commercial implementation of the technologies covered in this section: several DRAM dies (see the DRAM cell chapter) are connected via TSV and wafer-to-wafer or die-to-wafer bonding into a vertical stack of eight, twelve, or more layers, and then coupled to a logic die via a silicon interposer.

The resulting memory bandwidth far exceeds that of classical DRAM modules wired planarly on a circuit board, while also achieving significantly lower energy consumption per transmitted bit -- an effect that follows directly from the short, extremely densely packed interposer connection paths, translating the principles described earlier (TSV stacking, interposer wiring) directly into a measurable product advantage.

Modern AI training accelerators are the main driver behind the packaging technologies described here: they typically combine several logic chiplets with surrounding HBM stacks on a shared interposer, maximizing both the memory bandwidth critical to AI workloads and compute density, without depending on a single, uneconomically large monolithic die.

Particularly in AI training workloads, which must move enormous model parameters between compute units and memory, memory bandwidth is often the limiting factor for overall system performance -- not the raw compute power of the logic units. Advanced packaging directly addresses this bottleneck, which is why hardly any current AI accelerator gets by without interposer and HBM integration.