LM1117IMPX-3.3 National Semiconductor, LM1117IMPX-3.3 Datasheet - Page 3

• Manufacturer Part Number: LM1117IMPX-3.3
• Description: IC,VOLT REGULATOR,FIXED,+3.3V,BIPOLAR,SOT-223,3PIN,PLASTIC
• Manufacturer: National Semiconductor

Specifications of LM1117IMPX-3.3

• Rohs Compliant: NO

Sensitivity Analysis
So how do small changes in R1, R2, and V
output voltage? Sensitivity analysis reveals the underlying
nature of the circuit.
Taking the partial derivatives of equation 1 with respect to
each of its variables lets us calculate the sensitivity of V
small changes in each variable. This is done by dividing the
partial derviatives by V
into the equation, and finally solving for the fractional change
of V
three variables:
The result from equation 4 is obvious. That is, variation in
V
are a bit more interesting. These show that variations in the
voltage setting resistors R1 and R2 will translate to the
output with a sensitivity ranging from zero to one. The high-
est sensitivity to resistor variation occurs when output volt-
ages are high and lowest when the output voltage equals the
reference voltage (Figure 2).
FIGURE 2. – Sensitivity to Resistor Variations vs.V
ref
translate directly to variations in V
out
with respect to the fractional change in each of the
out
, then substituting equation 1 back
out
. Equations 5 and 6
out
translate to the
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out
out
(4)
(5)
(6)
to
3
Review of Random Variable
Mathematics
A few glances at Table 1 and it becomes clear that, for
adjustable regulators, the worst case deviations of V
be quite large. For example, look at the case where V
3.3 V, ∆V
output worst case error could be as high as ∆V
We can show that this number, although conservative, is a
gross exaggeration of the true variation in V
because V
ables. Some argue that R1 and R2 may not be independent
random variables. This is especially true if they are fed from
the same reel or supply bin. There is some truth to this,
however, R1 and R2 are rarely the same value and even if
they are, their sensitivities (equations 5 and 6) have equal
magnitudes and opposite polarities so any correlation would
tend to cancel rather than add!
Statistical Variation of Resistors,
Semiconductors, and Systems
To calculate actual variation, we will need to make some
assumptions about the statistics of V
buy. This information may be available from the vendor,
however, in many cases, the vendor may be reluctant to
release this data.
For fundamental electronic components like resistors it is
reasonable to assume that these are produced under a “six
sigma” paradigm and have Gaussian variation. Variations in
V
data for a typical linear regulator is shown in Figure 3 and
has variation against room temperature specifications on the
order of
specification is even more impressive and can be as high as
Because components like regulators and chip resistors are
made in very high volumes, tight process control is no less
than mandatory.
±
ref
10σ (to accommodate variations with temperature).
are also approximately Gaussian (Figure 3). Distribution
±
ref
ref
6σ. Variation against the full temperature range
=
FIGURE 3. Typical V
, R1, and R2 are all independent random vari-
±
1%, and ∆R =
±
1%. For this case, the total
ref
ref
Variation
and the resistors we
out
20148210
out
www.national.com
=
. This is
±
2.28%!
out
out
can
=