Category Archives: MEMS

Creating Value With “Track” Tools in Back end of the Line (BEOL)

The Semiconductor Industry has for years followed the path of “Moore’s” Law. Gordon Moore’s forecast that the industry would every 18 months double the number of transistors in a given area, thereby increasing value while reducing cost. His prediction has held true for about 20 years, and is now faltering. However the effect of his prediction can and will continue into the foreseeable future but rather than the aerial rule obtaining on silicon it will now move increasingly to the package. That trend is now beginning in earnest and the improvements in functionality and cost will occur more and more in the Back End of the Line (BEOL) /packaging of function. The driving economic factor is clearly the proliferation of mobile devices of all sorts.

While the technology of the photolithographic processing remains very similar there are important details that must be addressed if the tool sets of track and lithography are to be optimized for the new needs. BEOL processes involve different economic drivers, which must be accommodated by the Cost of Ownership (COO) models for BEOL tool sets versus Front End of Line (FEOL) tool sets. For more detail on the subject please see our BLOG entitled “Why Back End of Line (BEOL) Photoresist “Track” tool are and must be different from Front End of Line (FEOL) Photoresist processors.”

System Granularity and Management of Change and Growth

A very important aspect of cost of ownership (COO) of tools that include spin coaters and developers (often referred to a photoresist track tools) in the back end of line (BEOL), MEMS, and patterned sapphire substrate (PSS) processing-type processing lines is one that does not appear on the COO software packages. Often referred to as “opportunity cost,” it is specifically defined as “the difference in return between an investment one makes and another that one chose not to make.” A high degree of system “granularity” enables appropriate investment as customer needs evolve. Ultimately this can substantially reduce opportunity cost. Granularity specifically relates to tools sized to the capacity need, as well as properly balanced for output in order to minimize waste. Lets look at how that works, in the context of opportunity cost.

Since opportunity cost is always a comparison of the path chosen to the path that was not chosen, we will compare two hypothetical systems whose COO on a per wafer output basis is identical, but its granularity is different. Granularity will tend to go up in integer values. Let us suppose a system whose out put is equal to one unit of capacity, (i.e., 2000 wafers per month) and another whose out put is equal to two units of capacity (i.e., 4000 wafers per month). If the need is for one unit of capacity, then if the COO is the same and the more granular system is the clear choice. If it is two units of capacity then the choices are equal. However what happens when it is three units of capacity; then four; then five? Capacity needs often develop in incremental steps over the long term instead of in immediate doublings. The investment in the “granular approach” is obviously the more appropriate forward-thinking plan. As needs eventuate, so that capacity can be adjusted to market needs at the lowest possible cost in capital spread over time.