Transcript Optimizing Performance-Sensitive Semiconductor Products

USING JMP TO OPTIMIZE
PERFORMANCE-SENSITIVE
SEMICONDUCTOR
PRODUCTS
SCOTT RUBEL
TODD JACOBS
JIM NELSON
EXTERNAL USE
DISCOVERY SUMMIT 2016
Overview
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Modern chip designs have multiple IP components with different process, voltage,
temperature sensitivities
Optimizing mix to different customer requirements (bins) across process window
has huge gross margin impact
Monte Carlo simulation optimal for simulating speed-power distribution and bin mix
under different conditions
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Large number of statistical parameters (40 – 60 typically, plus covariance matrix) and bin limits
(~20-100)
− Consistent
− JMP
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extraction, documentation critical to manage these
scripting provides statistical tools + journaling = unique capability
Fast execution (1-5 minutes) and consistent documentation allow comparison of
multiple business cases
EXTERNAL USE
Simulation Flow
Vary scaling or limits to explore
different process scenarios,
evaluate profitability
• Software can simulate multiple
limit scenarios in one run
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EXTERNAL USE
Simulated
Cloud
Distributions
Process
Window
Optimal Spec,
Process
Limits
Alternate
scenarios
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Descriptive
Statistics
Alternate
scenarios
Product
Data or
Design
Inputs
Speed-Power Distributions
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Frequency is (nearly always) a linear
function of log(static current), plus noise
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EXTERNAL USE
3 parameters for power, plus 3 per frequency
Speed-Power Distributions
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EXTERNAL USE
“Distribution” is fraction of die meeting
minimum speed, maximum power specs for
a given spec, voltage
Speed-Power Distributions
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Testing spec at multiple voltages improves
distribution
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EXTERNAL USE
Lighter (darker) regions show additional die that
will pass when tested to higher (lower) voltage
Speed-Power Distributions
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EXTERNAL USE
Die that fail one spec can still pass another
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Red region: high-speed, high-power spec
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Blue region: low-speed, low-power spec
Speed-Power Distributions
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EXTERNAL USE
Some die can pass multiple specs
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Shaded areas show die which can pass combinations
of green, red, and blue specs
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Known as “combination (combo)” bins
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Can be used to flex supply from one bin to another
Complex boundaries require custom logic to
handle correctly
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Logical OR across voltages
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Logical AND across patterns and temperatures
Pass/Fail, Binning Calculations
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Overall rate can consider either all possible die which pass bin, or assign die to first
bin passed
Logic is straightforward, but tricky. Scripting ensures it is done right every time.
Die #
Pass/Fail results for multiple
bins, multiple die 
1
2
3
4
5
Max Dist
Bin Mix
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EXTERNAL USE
Bin 1
P
F
F
P
F
Bin 2
P
P
F
F
P
40%
40%
Bin 3
P
P
P
P
P
60%
40%
100%
20%
Bin 4
P
P
F
F
F
Sort Bin
B1
B2
B3
B1
B2
40% >100%
0% =100%
Sample Input Configuration File
Statistical parameters
saved to configuration
file after initial run
Configuration
parameters for
simulation runs
Limits for each pattern,
temperature, voltage
combination
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EXTERNAL USE
Data validity checks
•
JMP scripting ensures common analysis errors are avoided:
− Perfectly
correlated parameters
− Nonphysical
correlations
− Bin/pattern/grouping
− Missing
limits
− Missing
source data
− Missing/invalid
− Missing
parameters
grouping parameters (voltage, temperature, pattern)
− Missing/duplicate
− Missing
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combinations that guarantee zero distribution always
Fmax pattern names
test sort order
Red items are particularly easy to miss: simulation may run but produce deceptive results
EXTERNAL USE
Journal Output
X-Y plot shows
simulation for target
process condition
Input Table: /C:/Documents/data extracts/Feb 2015/Test.jmp
Each point represents
bin distribution for a
particular process
condition
Power limit voltage
sensitivities
documented
•
Journal file contains full configuration table, linear Fmax fit equations, and covariance tables
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X-Y plots can show any two input parameters desired, with appropriate limits
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Data tables for target process simulation and distributions automatically saved to specified location
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EXTERNAL USE
Scenario Modeling: Variance Scaling
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Factory process improvements affect bin distribution through tighter variance
Tool has Variance Scaling parameter to model this; included in standard
documentation
Generating plots requires separate runs
EXTERNAL USE
Scenario Modeling: Test Program Changes
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3
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5
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Original sort order: orange, red, light green, dark green, blue
Curves at right show yield to each bin given this order
− Sum
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is 100% (die not double-counted)
EXTERNAL USE
Scenario Modeling: Test Program Changes
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2
4
3
5
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Consider swapping red and dark green bins in sort order
New sort order: orange, dark green, light green, red, blue
Bin distributions change to new plot
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Note that light green distribution drops to zero while dark green increases dramatically
Red bin distribution also reduced
EXTERNAL USE
Scenario Modeling: Pre-Assembly Speed Sorting
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Based on wafer speed
testing these units are
built in a different
package
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EXTERNAL USE
Distributions for single
package look very
different than overall
mix
Script codes
simulated die based
on bin assignment,
allowing visual
assessment of how
parameters align
with binning
Scenario Modeling: Variable power limits
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B1 limits can be varied in single iterate run
• Here 11 different values of power limit used
• Distribution table can be used to create other graphs as desired
− Example
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shows impact of power limit on supply to Bin 1
EXTERNAL USE
Scenario Modeling: Frequency Loss
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Tool evaluates different aspects of distribution as frequency is impacted: process target
must be increased, process window shrinks, and maximum distribution drops
• Simulation shows up to ~40 MHz of speed loss tolerable. Key input for Design, Marketing
teams
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EXTERNAL USE
Summary
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Built-in validation manages complex inputs
• Math is taken care of for users, allowing focus on results
• Standard output ensures consistent, thorough documentation
• Can be seeded with data prior to tapeout to ensure successful product launch
− Significant
− Concrete
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feedback to Marketing and Design teams enhances customer engagement
Tool allows rapid evaluation of different production scenarios
− Easy
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cost savings, revenue improvement
to perform sensitivity analyses
EXTERNAL USE