Transcript slides

A DSP-Based Ramp Test for
On-Chip High-Resolution ADC
Wei Jiang and Vishwani D. Agrawal
Auburn university
Motivation
• Testing analog-to-digital converter (ADC)
– Linear ramp to cover full range of ADC
– Slow slope for static testing
– Histogram-based non-linearity error
• Proposed DSP-based ramp test
– Characterizing ADC using linear function
– Estimating coefficients of the function
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Non-linearity Error
• Least significant bit
 ˆk   k  0.5  LSB
(LSB)

ˆk 1   k  0.5  LSB

• Signal values at lower
ˆk ˆk 1
and upper edges of each  k 
2
codes
ˆk  2 ˆk
DNLk 
 LSB
• Non-linearity errors
2
– Differential non-linearity
(DNL)
– Integral non-linearity
(INL)
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k
ˆk ˆk 1
i 1
2
INLk   DNLk 
 k
3
Typical ADC Testing Architecture
Analog
Loopbacks
DAC
under-test
MUX
under-test
ANALOG
SYSTEM
MUX
ADC
TPG
Test pattern
control
Loopback
controls
DSP
TEST
CONTROL
Response
control
MUX
MUX
Analog input
Digital output
Analog signals
Digital loopback
ANALOG
SYSTEM
Analog loopback
Analog
System
Input
and
Output
Analog system loopback
Analog output
ORA
DIGITAL
SYSTEM
Digital
System
Input and
Output
Digital input
under-test
MIXED SIGNAL
BIST
Results
* F. F. Dai and C. E. Stroud, “Analog and Mixed-Signal Test Architectures,” Chapter 15, p. 722 in
System-on-Chip Test Architectures: Nanometer Design for Testability, Morgan Kaufmann, 2008.
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Ramp Test Structure for ADC
Similar to structures for histogram approach
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Ramp Test
• Histogram Test for high-resolution ADC
– A large amount of code to be tested
– Multiple samples for each code
– Very low-slope ramp testing signals required
• Comparable to thermal noise
• Proposed Approach
– Linear function to characterize ADC
– Coefficients of the function are easy to calculate
– Only part of codes measured; speed up testing time
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Linear Function
• Linear ramp function
f k   a  T  k  b
– T is the sampling time
• Measured output
– K is the maximum
code
• Reconstructed ramp
function using
measured samples
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K 0
0

M k  k 0K M ADC  f k  k  1..K  1
2 N  1
kK

fˆ k   M k   LSB  eq
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Division of Measurements
• Divided full-range of
ADC codes into two
equal-size sections
• Sum up measurements
of each section
• Lower bound M(0) and
upper bound M(K) are
discarded because of
possible out-of-range
measurements
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K /2
1 K /2

 s0   M k   LSB  f k 
k 1
k 1


1 1
1




K
K

2
aT

Kb



LSB  8
2





K 1

1 K 1
f k 

s0   M k  
LSB k  K / 2
k K / 2


1 1
1




K
2
K

2
aT

Kb



LSB  8
2


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Calculation of Coefficients
• Two syndromes
obtained from sums
• LSB, K and T are all
known parameters
• Estimated coefficients
calculated from
syndromes
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S 0  s1  s0

  1  1 K K  2aT 

LSB  4



 S   s  3s
1
0
 1
1

  LSB K aT  b 
4S0
1
1

a  LSB K K  2  T

1 S1 K  2 S1  4 S 0
b 

LSB
K K  2 
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BIST Steps - measurements
• Reset ramp testing signal generator
• Detect first non-zero ADC output (lowerbound of samples)
• Measure all subsequent samples
• Stop at the maximum ADC output (upperbound of samples)
• DSP collects all valid measurements and start
to processing data
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BIST Steps – processing data
• Divide measured samples into two equal-size
parts
• Accumulate measurements of each part to
obtain two sums
• Calculate two syndromes from two sums
• Calculate two estimated coefficients of the
linear ramp function
• (Optional) Compare each measured data to
estimated one from ramp function
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Design of Ramp Signal Generator
ΔV+Vth
ΔV
I/30
Range: 0 v~ Vdd-ΔV
I
I
Switch for
resetting
ramp
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Simulation Results
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Simulation Results
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Other considerations
• Minimal number of samples
– More samples, less quantization noise, more accurate
estimation
– Not all codes need to be sampled in order to reduce
testing time
– At least 2N-2 samples are found necessary in practice
• The same idea may be used with low-frequency
sinusoidal testing signals instead of ramp signal
– More overhead and complexities with sinusoidal
generator
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Conclusion
• Proposed Approach
– For high-resolution ADC
– Less samples required comparing to histogram
approach
– Simple algorithm to calculate coefficients and
make estimation
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THANK YOU
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