University of Texas at Austin
Dr. Flamm said he would address the “stylized facts” that should be considered in trying to model the semiconductor industry, as well as some of the modeling work under way at SEMATECH and the University of California at Berkeley. He began by listing some key economic features of the semiconductor industry that might be associated with deep cyclical swings.
The first is rapid technical progress. This means that holding inventories as a way of smoothing out demand fluctuations is not productive; because of the short lifetimes of semiconductor products, the inventories will lose value and even become obsolete if they are held too long. Instead, firms must sell what they have as quickly as they can.
Second, very large R&D investments are required to enter this industry—typically, as much as 10 to 15 percent of annual sales. This R&D is often specific to the segment of the market that the firm is entering.
Learning economies are very important. For military aircraft, a learning curve of 85 to 90 percent is estimated, which means a doubling of output drops unit costs by 10 to 15 percent. In semiconductors this curve is closer to 70 percent, quite a bit steeper. In addition, the source of the learning economy in semiconductors is different from that in aircraft.
The curve is not caused necessarily by labor productivity. Instead, improvements come from two sources. One is die-shrinks: Over the cost of a product cycle, the number of chips on a wafer increases. Over any product’s life, this happens typically two or three times, essentially increasing the product on each silicon wafer. The other source is yield learning: The number of good chips on the wafer increases over time as a percentage of the total number of chips. Together these sources generate the steep learning curve. This curve is thought to have flattened somewhat, but good evidence of this is difficult to come by.