The Truth Behind Intel's Manufacturing Lead

Business Quant profile picture
Business Quant


  • Real nodes are apparently different from the advertised nodes.
  • But even after normalizing nodes, we see that Intel is set to lose its process leadership post-2018.
  • This may boost the appeal factor of GlobalFoundries, TSMC and Samsung and force Intel to slash its prices to stay competitive.

The longevity of Intel's (NASDAQ: NASDAQ:INTC) manufacturing lead is being questioned rigorously of late with its competitors coming up with smaller nodes on aggressive timelines. Bullish investors and tech enthusiasts argue that TSMC (TM), Samsung (OTCPK:SSNLF) and GlobalFoundries have process nodes that are far inferior than their advertised figures but it doesn't really help Intel's case by a great deal. I did some sleuthing of my own and found that even if we create normalized-node (not advertised node) roadmaps of all four companies, Intel's process lead would vanish in the next two years. Let's take a closer look.

(Image Source)

Normalizing Nodes

Let me start by saying that process nodes advertised by Intel, Taiwan Semiconductor, GlobalFoundries and Samsung are drastically different from each other in reality. For starters, TSMC's 16nm process reportedly uses a 20nm backbone. Chips fabricated by each of the fabs tend to have parts with dimensions vastly varying from each other. These advertised nodes have been basically reduced to just marketing material for the aforementioned semiconductor firms and the advertised figures aren't an accurate representation of physical dimensions used by any means. In fact, a chip manufactured on the 14nm node by Samsung or GlobalFoundries may not have any part that exactly scales 14nm.

The current state of semiconductor manufacturing is such that nobody follows a universal node naming process. In a bid to simplify this confusing scenario, ASML, which is the world's largest supplier of photolithography equipment for semiconductor manufacturers, has reportedly come up with a formula to normalize nodes based on their effective feature sizes. Granted that the formula is far from perfect and doesn't factor in other features such as SRAM cell sizes, but it's currently the best resource available to us to separate reality from marketing FUD. The calculation goes as follows:


This article was written by

Business Quant profile picture
Business Quant is a comprehensive investment research platform. It hosts KPI data, financial data and analytical tools to help you become a better investor. You don’t have to go through boring SEC filings to keep a track of AT&T’s subscriber count, Apple’s revenue from iPhones or Disney’s revenue by region. Our Granular KPI Data tool does that for you and it does so much more. Get an edge over the market, from day one. Watch Business Quant in action here.

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Recommended For You

Comments (52)

To ensure this doesn’t happen in the future, please enable Javascript and cookies in your browser.
Is this happening to you frequently? Please report it on our feedback forum.
If you have an ad-blocker enabled you may be blocked from proceeding. Please disable your ad-blocker and refresh.