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Explore model-based test thresholds, reduction of yield loss, defect level, and components' statistical variation. Includes Monte Carlo simulation for operational amplifiers testing. References on analog performance prediction.
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VLSI TestingLecture 12: Alternate Test Dr. Vishwani D. Agrawal James J. Danaher Professor of Electrical and Computer Engineering Auburn University, Alabama 36849, USA vagrawal@eng.auburn.edu http://www.eng.auburn.edu/~vagrawal IIT Delhi, July 30, 2012, 4:00-5:00PM Lecture 12: Alternate Test
Contents • Setting thresholds in model-based test • Alternate test • Summary • References Lecture 12: Alternate Test
Setting Thresholds in Model-Based Test • In model-based test, component values are determined. • Preset “thresholds” for component variation classify the device under test as good or faulty. • How do we determine the “thresholds”? • For example, • Circuit is good if R1’ ≤ R1 ≤ R1’’ Lecture 12: Alternate Test
An Operational Amplifier R2 R1 + _ Gain = V2/V1 = R2/R1 V1 V2 Lecture 12: Alternate Test
Pessimism in Model-Based Test Yield loss Slope = G R2 Only good devices accepted 0 R1 0 Lecture 12: Alternate Test
Reducing Yield Loss Slope = G Reduced yield loss R2 Faulty devices accepted 0 0 R1 Lecture 12: Alternate Test
Yield Loss and Defect Level • Yield loss: Amount of yield reduction because some good devices fail non-functional tests. • Defect level (DL): Fraction of faulty devices among those that pass non-functional tests. • Example: 1,0000 devices are fabricated. 7,000 are good. True yield, y = 0.7. Test passes 6,900 good and 150 bad devices. Then, • Yield loss = (7,000 – 6,900)/10,000 = 0.01 or 1% • DL = 150/(6,900+150) = 0.02128 or 2.128% or 21,280 DPM (defective parts per million) Lecture 12: Alternate Test
Yield Loss and Defect Level All fabricated devices Devices passing test Good devices Defect level Yield loss Lecture 12: Alternate Test
Component Variation (Statistical) Uniform Gaussian Mean Mean Component (R or C) value Component (R or C) value Lecture 12: Alternate Test
Monte Carlo Simulation • Consider operational amplifier example. • R1 and R2 are random variables with given (uniform or Gaussian) probability density functions with • Mean = nominal value • Standard deviation based on manufacturing data • Generate large number of samples for R1 and R2 • Simulate each sample using spice • Determine gain for each sample • For each set of tolerance limits, determine yield loss and defect level. Lecture 12: Alternate Test
Monte Carlo Simulation Data Slope = G R2 0 R1 0 Lecture 12: Alternate Test
Setting Test Limits Minimize yield loss Slope = G R2 Minimize defect level 0 0 R1 Lecture 12: Alternate Test
Alternate Test • Besides components (e.g., R1 and R2 for operational amplifier) easily measurable parameters used for testing. • An example is the supply current IDD of the operational amplifier. • A simple test is to measure IDD(0) for 0V input. • Monte Carlo simulation is then used to set the limits on IDD(0). • Large number of sample circuits with component variations are simulated to determine thresholds for IDD(0). • Additional measurements can improve test. Lecture 12: Alternate Test
Alternate Test: Setting Thresholds Minimize yield loss Within spec. gain Gain Minimize defect level Fail Pass Fail 0 0 IDD(0) Lecture 12: Alternate Test
References • P. N. Variyam, S. Cherubal and A. Chatterjee, “Prediction of Analog Performance Parameters Using Fast Transient Testing,” IEEE Trans. Computer-Aided Design, vol. 21, no. 3, pp. 349-361, March 2002. • H.-G. Stratigopoulos and Y. Makris, “Error Moderation in Low-Cost Machine-Learning-Based Analog/RF Testing,” IEEE Trans. Computer-Aided Design, vol. 27, no. 2, pp. 339-351, February 2008. Lecture 12: Alternate Test
