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56 DESIGN007 MAGAZINE I AUGUST 2025 demand. Three factors can control the maximum wiring demand: 1. The wiring required to break out from a com- ponent like a flip chip or chip-scale package. 2. The wiring created by two or more compo- nents is tightly linked, like a CPU and cache, or a DSP and its I/O control. 3. The wiring demanded by all integrated cir- cuits and discretes collectively. There are models available to calculate the com- ponent wiring demand for all three cases. Since it is not always easy to know which case controls a design, I usually calculate all three cases to see which is the most demanding and thus controls the layout. The model I find most useful for Case 3 is Coors and Anderson's statistical wiring requirement. 2 Other widely used models are: • Rent's Rule Technique 3 • Toshiba Technology Map 4 • Donath Method 5 • Section Crossing Method 6 • Geometric Approach 7 Each has circuit topology conditions that will differentiate which model to use. All these models are described in the HDI Handbook. Getting Over the Density Wall To achieve higher routing density, there are only five degrees of freedom: • Smaller traces • Traces closer together (spaces) • Smaller vias (down to microvias) • Smaller annular rings for the vias • Higher layout efficiency when routing 8 I am talking about routing density on a single layer; more signal layers will result in a greater total routing distance on a board. The equation for routing density is (Equation 1): where: N = number of traces in the channel D a = via annular ring G = Routing channel dimension C s = conductor spacing D v = via diameter (FHS) C w = conductor width If you reduce some of the variables in Equation 1, the resulting routing density will increase. The larg- est effect on density is reducing the trace width, but this can come with electrical issues. The next best way to increase density is to reduce the via's annular ring, but a very small AR will significantly reduce the via's reliability. Coors and Anderson's Statistical Wiring Requirements This wiring demand model is based on a stochastic model involving all terminals. The probable wire length is calculated based on the distance of a second terminal and the spatial geometry of all ▼ F i g u re 2 : C o o rs a n d A n d e rs o n's w i r i n g m o d e l w i t h f i g u re exa m p l e a n d w i r i n g s e g m e nt s .