Optimization Strategies for Performance-Sensitive Semiconductor Products
Discover how using JMP can optimize semiconductor product performance across various conditions, emphasizing the impact on gross margin. Learn about the benefits of Monte Carlo simulation, statistical analysis, and consistent documentation in managing complex chip designs and customer requirements effectively.
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USING JMP TO OPTIMIZE PERFORMANCE-SENSITIVE SEMICONDUCTOR PRODUCTS DISCOVERY SUMMIT 2016 SCOTT RUBEL TODD JACOBS JIM NELSON EXTERNAL USE
Overview 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 Large number of statistical parameters (40 60 typically, plus covariance matrix) and bin limits (~20-100) Consistent extraction, documentation critical to manage these JMP scripting provides statistical tools + journaling = unique capability Fast execution (1-5 minutes) and consistent documentation allow comparison of multiple business cases 1 EXTERNAL USE
Simulation Flow Product Data or Design Inputs Descriptive Statistics Limits Simulated Cloud scaling Alternate Alternate scenarios scenarios Distributions Vary scaling or limits to explore different process scenarios, evaluate profitability Software can simulate multiple limit scenarios in one run Process Window Optimal Spec, Process 2 EXTERNAL USE
Speed-Power Distributions Frequency is (nearly always) a linear function of log(static current), plus noise 3 parameters for power, plus 3 per frequency 3 EXTERNAL USE
Speed-Power Distributions Distribution is fraction of die meeting minimum speed, maximum power specs for a given spec, voltage 4 EXTERNAL USE
Speed-Power Distributions Testing spec at multiple voltages improves distribution Lighter (darker) regions show additional die that will pass when tested to higher (lower) voltage 5 EXTERNAL USE
Speed-Power Distributions Die that fail one spec can still pass another Red region: high-speed, high-power spec Blue region: low-speed, low-power spec 6 EXTERNAL USE
Speed-Power Distributions Some die can pass multiple specs Shaded areas show die which can pass combinations of green, red, and blue specs Known as combination (combo) bins Can be used to flex supply from one bin to another Complex boundaries require custom logic to handle correctly Logical OR across voltages Logical AND across patterns and temperatures 7 EXTERNAL USE
Pass/Fail, Binning Calculations 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 # Bin 1 1 P 2 F 3 F 4 P 5 F Bin 2 P P F F P Bin 3 P P P P P Bin 4 P P F F F Sort Bin B1 B2 B3 B1 B2 Pass/Fail results for multiple bins, multiple die Max Dist Bin Mix 40% 40% 60% 40% 100% 20% 40% >100% 0% =100% 8 EXTERNAL USE
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 9 EXTERNAL USE
Data validity checks JMP scripting ensures common analysis errors are avoided: Perfectly correlated parameters Nonphysical correlations Bin/pattern/grouping combinations that guarantee zero distribution always Missing limits Missing source data Missing/invalid parameters Missing grouping parameters (voltage, temperature, pattern) Missing/duplicate Fmax pattern names Missing test sort order Red items are particularly easy to miss: simulation may run but produce deceptive results 10 EXTERNAL USE
Each point represents bin distribution for a particular process condition Journal Output X-Y plot shows simulation for target process condition Input Table: /C:/Documents/data extracts/Feb 2015/Test.jmp Power limit voltage sensitivities documented Journal file contains full configuration table, linear Fmax fit equations, and covariance tables X-Y plots can show any two input parameters desired, with appropriate limits Data tables for target process simulation and distributions automatically saved to specified location 11 EXTERNAL USE
Scenario Modeling: Variance Scaling 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 12 EXTERNAL USE
Scenario Modeling: Test Program Changes 2 1 3 4 5 Original sort order: orange, red, light green, dark green, blue Curves at right show yield to each bin given this order Sum is 100% (die not double-counted) 13 EXTERNAL USE
Scenario Modeling: Test Program Changes 2 4 1 3 5 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 Note that light green distribution drops to zero while dark green increases dramatically Red bin distribution also reduced 14 EXTERNAL USE
Scenario Modeling: Pre-Assembly Speed Sorting Script codes simulated die based on bin assignment, allowing visual assessment of how parameters align with binning Based on wafer speed testing these units are built in a different package Distributions for single package look very different than overall mix 15 EXTERNAL USE
Scenario Modeling: Variable power limits 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 shows impact of power limit on supply to Bin 1 16 EXTERNAL USE
Scenario Modeling: Frequency Loss 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 17 EXTERNAL USE
Summary 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 cost savings, revenue improvement Concrete feedback to Marketing and Design teams enhances customer engagement Tool allows rapid evaluation of different production scenarios Easy to perform sensitivity analyses 18 EXTERNAL USE
