The curious case of the 7% resistors

The curious case of the 7% resistors

Ever have an application where you needed resistors with accurate values, but couldn’t find any in your pile of cheap ones that had exactly the value was marked on them? Howard Johnson of the Electronics Design, Strategy, News blog has a surprising explanation for the phenomenon in his article, the 7% solution:

The drawing complete, Martin said, “A 10% carbon-composition resistor is made in a somewhat slipshod manner. The manufacturer tries to get it right, but some of the variables are just too difficult to control. They make up a batch, test them all, and then throw away the bad ones. What’s left is a distribution of values truncated on either side at the ±10% limits. The other main feature of the distribution is the big gap-toothed section in the middle. That’s where they pulled out all the good parts and sold them at a higher price with a ±5% tolerance. How else do you think they make 5% resistors?”

I’ve never taken the time to measure a batch of resistors to verify that this is true, however it certainly sounds plausible. Anyone here have some first hand experience with this? It seems like it would be a great exercise to go through in a practical electronics class. [via mightyohm]

6 thoughts on “The curious case of the 7% resistors

  1. says:

    Lots of electronics are made with a similar attitude. First you manufacture something in bulk, then you test the product and price it according to how fast/error-free/close-to-tolerance it is.

    2 examples;

    Any given model of CPU comes in multiple speeds. They are all really from the same manufacturing line. In the test harness, the manufacturer runs the CPUs up to the various standard speeds. If it succeeds at a lower speed and fails at a higher speed, the part is marked for distribution as the lower speed, cheaper, SKU.

    For memory, a manufacturer will generally try to run a production line targeting the higher margin, higher capacity, parts. If a part fails, it is determined if a particular bank of bits are bad. If they are and if the addressing lines can be rewired to effectively turn off that bank, the part will be sold as a lower capacity, cheaper, component. If a part is failed because the error rate is too high for, say, operation in a computing device, it may be packaged and sold into applications that can handle the high error rate. For example, the memory used in voice recorders doesn’t really have to be that terribly bulletproof as it is generally recording a pretty darned low quality signal in the first place. A few extra bits of error here and there just doesn’t make a difference.

    It really is a very fascinating sector of industry. I’m sure there are many more examples (and I’m sure my examples have been further optimized in the years since I first learned of them).

  2. boduelmike says:

    So, it boils down to whether it’s cheaper/easier to use two lower value 7% resistors in series, or a selected 5% value.

    Then, of course, only some of the %5 ones you have will be good enough, so there’s a wastage rate down that route – and how many pairs of the 7% examples falling in the limits you need can be made from the distribution you’ve got. Allowing for these factors…

    My brain hurts.

  3. JasonOT says:

    Everything bbum posted is spot on, but it isn’t just in the electronics industry. It’s in EVERY industry that has a manufacturing arm.

    Any production piece that is sold with different tolerances is made with the same statistical approach. You aim for dead nuts, and sell the out of tolerance stuff cheaper at a wider tolerance.

    The Six Sigma approach is often used, and modified, to refine manufacturing processes so that pieces of all available tolerances can be manufactured in appropriate volumes.

    For example: Widget A is sold at 5% and 10% tolerances, with the 10% A Widgets outselling 5% 10:1; Widget B is also sold with 5% & 10%, but B’s 10% only outselling 2:1. A savvy manufacturer will refine their processes for A to have a greater standard deviation than B, so they can meet the volume needs of the 10% A Widgets.

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