1 rasit onur topaloglu and alex orailoglu {rtopalog|alex}@cse.ucsd.edu university of california, san...
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Rasit Onur Topaloglu and Alex Orailoglu {rtopalog|alex}@cse.ucsd.eduUniversity of California, San Diego
Computer Science and Engineering Department9500 Gilman Dr., La Jolla, CA, 92093
Forward Discrete Probability Propagation for Device Performance Characterization under
Process Variations
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Outline
Forward Discrete Probability Propagation
Probability Discretization Theory
Q, F, B, R and Q-1 Operators
Experimental Results
Conclusions
Motivation and Comparison with Monte Carlo
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Motivation
•Process variations have become dominant even at the device level in deep sub-micron technologies
•To reduce design iterations, there is a need to accurately estimate the effects of process variations on device performance
•Current tools/methods not quite suitable for this problem due to accuracy & speed bottlenecks
•Most simulators use SPICE formulas
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SPICE Formula Hierarchy
ex: gm=2*k*ID
Level1
Level4
Level0TLeffWefftox
Level2
Level3
0 NSUBms
CoxF
k Qdep
Vth
ID
gm rout Level5
•SPICE formulas are hierarchical; hence can tie physical parameters to circuit parameters
•A hierarchical tree representation possible : connectivity graphs
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Process Variation Model
•Physical parameters correspond to the lowest level in connectivity graphs
•Recent models attribute process variations to physical parameters
•Probability density functions (pdf’s), acquired through a test chip, can be independently input to the lowest
level nodes
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Probability Propagation
•Estimation of device parameters at highest level needed to examine effects of process variations•An analytic solution not possible since functions highly non-linear and Gaussian approximations not accurate in deep sub-micron
GOALS
Algebraic tractability : enabling manual applicability
Speed : be comparable or outperform Monte Carlo
•A method to propagate pdf’s to highest level necessary
Flexibility : be able to use non-standard densities
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Monte Carlo for Probability Propagation
gm
Level1
Level4
Level0 nVFBNSUBLW
Vth Cox
tox
ID
k Level2
Level3
•Pick independent samples from distributions of Level0 parameters•Compute functions using these samples until highest level reached •Iterate by repeating the preceding 2 steps•Construct a histogram to approximate the distribution
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Shortcomings of Monte Carlo
•Not manually applicable due to large number of iterations and random sampling
•Limited to standard distributions : Random number generators in CAD tools only provide certain distributions
•Accuracy : May miss points that are less likely to occur due to random sampling; a large number of iterations necessary which is quite costly for simulators
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Implementing FDPP
•F (Forward) : Given a function, estimates the distribution of next node in the formula hierarchy using samples
•Q (Quantize) : Discretize a pdf to operate on its samples
Analytic operation on continuous distributions difficult; instead work in discrete domain and convert back at the end:
•Q-1 (De-Quantize) : Convert a discrete pdf back to continuous domain : interpolation
•B (Band-pass) : Used to decrement number of samples using a threshold on sample probabilities
•R (Re-bin) : Used to decrement number of samples by combining close samples together
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T NSUB
PHIf
Necessary Operators (Q, F, B, R) on a Connectivity Graph
Q Q
F
B
•F, B and R repeated until we acquire the distribution of a high level parameter (ex. gm)
R
Q-1
TLeffWefftox 0 NSUBms
CoxF
k
Vth
ID
gmrout
Qdep
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pdf(X)
Probability Discretization Theory: QN Operator
•QN band-pass filter pdf(X) and divide into bins
))(()( XpdfQX N
N in QN indicates number or bins
spdf(X)=(X)X
pdf(
X)
spdf
(X)
X
Ni
ii wxpX..1
)()(
•can write spdf(X) as :
where :
pi : probability for i’th impulse
wi : value of i’th impulse
•Use N>(2/m), where m is maximum derivative of pdf(X), thereby obeying a bound similar to Nyquist
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Error Analysis for Quantization Operator
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2dqQpdfqQEQ )(][
2/
2/
222
Variance of quantization error:
•If quantizer uniform and small, quantization error random variable Q is uniformly distributed
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F Operator •F operator implements a function over spdf’s using
deterministic sampling
))(),..,(()( 1 rXXFY Xi, Y : random variables
r
r
rss
Xs
Xs
Xs
Xs wwfyppY
,..,1
1
1
1
1
1
1)),..,((..)(
•Corresponding function in connectivity graph applied to deterministic combination of impulses•Heights of impulses (probabilities) multiplied•Probabilities are normalized to 1 at the end
pXs : probabilities of the set of all samples s belonging to X
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Effect of Non-linear Functions
•Non-linear nature of functions cause accumulation in certain ranges
Band-pass and re-bin operations needed after F operation
Impulses after F, before B and R
•De-quantization would not result in a correct shape•Increased number of samples would induce a computational burden
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))(()(' XBX e
Band-pass, Be, Operator
•Eliminate samples having values out of range (6): might cut off tails of bi-modal or long-tailed distributions
Margin-based Definition:
Novel Error-based Definition:•Eliminate samples having probabilities least likely to occur :
eliminates samples in useful range hence offers more computational efficiency
))(()
)(max(:
)()(Xp
e
ppi
ii
iii
i
wxpX
e : error rate
•Implementation : eliminate samples with probabilities less than 1/e times the sample with the largest probability
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Re-bin, RN, Operator ))(()(' XRX N
Impulses after F Resulting spdf(X)Unite into one bin
•Samples falling into the same bin congregated in one
i
ii wxpX )()( ijs
ji bwstppj
.where :
•Without R, Q-1 would result in a noisy graph which is not a pdf as samples would not be equally separated
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Error Analysis for Re-bin Operator
jbjiji
ji pwmdi
)(:,
),(Total distortion:
2)(),( jiji wmwmd Distortion caused by representing samples in a bin by a single sample:
mi : center or i’th bin
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Experimental Results
•Impulse representation for threshold voltage and transconductance are obtained through FDPP on the graph
(X) for gm(X) for Vth
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19•A close match is observed after interpolation
Monte Carlo – FDPP Comparison
solid : FDPP dotted : Monte Carlo
Pdf of VthPdf of ID
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Monte Carlo – FDPP Comparison with a Low Sample Number
•Monte Carlo inaccurate for moderate number of samples•Indicates FDPP can be manually applied without major accuracy degradation
solid : FDPP with 100 samples
Pdf of FPdf of F
noisy : Monte Carlo with 1000 samples
solid : FDPP with 100 samples
noisy : Monte Carlo with 100000 samples
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Conclusions•Forward Discrete Probability Propagation is introduced as
an alternative to Monte Carlo based methods
•FDPP should be preferred when low probability samples need to be accounted for without significantly
increasing the number of iterations
•FDPP provides an algebraic intuition due to deterministic sampling and manual applicability due to using less number of samples
•FDPP can account for non-standard pdf’s where Monte Carlo-based methods would substantially fail in terms of accuracy