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7/24/2019 Lecture21 Matching 2up http://slidepdf.com/reader/full/lecture21-matching-2up 1/12 EECS240 – Spring 2012 Lecture 21: Matching Elad Alon Dept. of EECS EECS240 Lecture 21 2 Offset To achieve zero offset, comparator devices must be perfectly matched to each other How well-matched can the devices be made? Not arbitrary – direct function of design choices V i+ V i-

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EECS240 – Spring 2012

Lecture 21: Matching

Elad AlonDept. of EECS

EECS240 Lecture 21 2

Offset

• To achieve zero offset, comparator devicesmust be perfectly matched to each other 

• How well-matched can the devices be made?• Not arbitrary – direct function of design choices

Vi+ Vi-

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EECS240 Lecture 21 3

Device Mismatch Categories

• Die-to-die• All devices on same chip (or wafer) have same

characteristics

• Within die (long-range)• All devices within certain region have same

characteristics

• Local (short-range)• Every device different, random

• Usually most important source of mismatch

EECS240 Lecture 21 4

Sources of Local Variation• Deterministic sources:

• Local poly density

• Sub-90nm: stress, litho interactions, …

• Random sources:

• Dopant fluctuations

• Line-edge roughness

• Oxide traps

• Focus our modeling on random variations

• Deterministic handled with good layout practices

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EECS240 Lecture 21 5

References

• M. J. M. Pelgrom, A. C. J. Duinmaijer, and A. P. G.Welbers, "Matching properties of MOS transistors," IEEE

JSSC, vol. 24, pp. 1433 - 1439, Oct. 1989

• Mismatch model

• Statistical data for 2.5 m CMOS

• J. A. Croon, M. Rosmeulen, S. Decoutere, W. Sansen, and

H. E. Maes, “An easy-to-use mismatch model for the MOS

transistor,” IEEE JSSC, vol. 37, pp. 1056 - 1064, Aug. 2002

• 0.18 m CMOS data

EECS240 Lecture 21 6

Mismatch Statistics• Total mismatch set by composite of many

single, independent events

• Correlation distance << device dimensions

• E.g., number of dopant atoms implanted into the

channel

• Individual effects are small: linear superposition

holds

•  Mismatch is zero mean, Gaussian distribution

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EECS240 Lecture 21 7

Parameter Mismatch Model

222

2

 xP

P DS 

WL

 AP    

2 : variance of P

: active gate area

: distance between device centers

: measured area proportionality constant

: measured distance proportionality constant,

:

 x

P

P

P

WL

 D

 A

   

  0 for "good" layout

EECS240 Lecture 21 8

VT Mismatch

2

2 2 2T 

T V x

 AV S D

WL   

,

,

2.5μm CMOS process :

30 mV m

35 mV m

V N MO S  

V P MO S  

 A

 A

 

 

• Mismatch in VT between

two identical devices:

• Often largest source of 

offset

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EECS240 Lecture 21 9

Drain Bias, VDS

 

VT largely independent of VDS

EECS240 Lecture 21 10

Back-Gate Bias, VSB

• Mismatch can

depend on VSB

• Why?

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EECS240 Lecture 21 11

Current Matching, ID /ID

Strong bias dependence (we knew that already)

EECS240 Lecture 21 12

Current Factor 

 L

W C 

ox    

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EECS240 Lecture 21 13

Sources of β Mismatch

• Mobility variations• E.g., due to dopant variations, random defects

• Oxide thickness variation

• Usually very well-controlled

• Edge roughness

EECS240 Lecture 21 14

Edge Model

For:

Simplifies to:

2

2

2

2

2

2

2

2

2

2

n

n

ox

ox

 L

 L

 

     

  

   

2222

2

2

2

2

2

2

 DS WL

 A

WL

 A

 LW 

 A

WL

 Aox

C W  L

  

 

  

   

 L L

W  1 and 1 22     

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EECS240 Lecture 21 15

Orientation Effects

• Si and transistors are not

(perfectly) isotropic

•  keep direction of 

current flow same!

EECS240 Lecture 21 16

Distance Effect

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EECS240 Lecture 21 17

Process Dependence

• AVt tends to scale

with technology

• Proportional to tox

• Also depends on

doping level

EECS240 Lecture 21 18

0.18 m CMOS

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EECS240 Lecture 21 19

Current Matching

EECS240 Lecture 21 20

Voltage Matching

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EECS240 Lecture 21 21

“Golden Rule” of Layout for Matching

• Everything you can think of might matter 

• Even whether or not there is metal above the

devices

• How to avoid systematic errors?

Ref: A. Hastings, “The art of analog layout,” Prentice Hall, 2001

EECS240 Lecture 21 22

Common Centroid Layout• Cancels linear gradients

• Required for moderate matching

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EECS240 Lecture 21 23

Simulating Mismatch

• Brute force: Monte Carlo

• HSPICE “throws the dice”…

EECS240 Lecture 21 24

Simulating Mismatch