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### A DFT Approach for Diagnosis and Process Variation-Aware Structural Test of Thermometer Coded Current Steering DAC's

Rasit Onur Topaloglu and Alex Orailoglu{ rtopalog | alex [email protected] of California, San DiegoComputer Science and Engineering Department 9500 Gilman Dr., La Jolla, CA, 92093

Outline

- Current Steering Digital to Analog Converters 101
- A Process Variation-Aware Soft Fault Model
- Process Variation Estimation
- Reduction of Diagnosis Time Using Design for Testability Hardware
- Experimental Results

Introduction

- Higher precision applications drive Digital to Analog Converter (DAC) resolutions to higher bits day by day
- Higher bit resolutions increase circuit complexity, hence increase test time and difficulty
- In thermometer coded circuits, controllability is limited as each bit increment sums current of a new source with previous ones
- Diagnosis of a fault or test is usually handled by exhaustively trying all input codes

Binary-Coded Current Steering Principle

- Input digital code selects current sources to be added to analog output
- Iout is the analog output
- Current sources are in fact implemented by current mirrors using a common on-chip reference current

4I

2I

I

(110) input shown for an 8-bit binary CSDAC (Current Steering DAC)

Iout

Individual Current Distributions

- Soft faults for exponentially valued current sources would contribute integral and differential non-linearity (INL and DNL) degradation during certain transitions e.g. transition from 2^n-1 to 2^n

W1/L1

W2/L2

W1/L1

W2/L2

4I

Iref

I

Iref

4I

I

Impact of Binary Coding on INL and DNL

Analog

- Transitions from 2^n-1 to 2^n activate totally differents sets of current sources
- Due to limited spatial correlation between these groups, DNL will tend to get larger, which implicitly tend to enlarge INL
- In thermometer-coded (TC-CSDACs), in these transitions, one more current source is added only, and hence outputs of these two codes highly correlated due to the 2^n-1 common elements

INL: max difference between overall real and ideal lines

DNL: max of stepwise differences

Digital

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8

Thermometer-Coded CSDACs (TC-CSDACs)

- (110) in binary is (0111111) in thermometer code
- Equal weighting of current sources prevents significant impact for faulty sources
- Error correction capability is another attractive reason for choosing thermometer code ex:0111011 not possible as 1’s should be consecutive

I

I

I

I

I

I

I

I

(0111111) input for an 8-bit thermometer coded CSDAC

Iout

Diagnosis Restriction of TC-CSDACs

- Current sources indexed with fixed bit positions
- Fixed indexes imply a controllability restriction
- Diagnosis time for a faulty current source exponentially increases as compared to binary coded CSDACS

I

I

I

I

I

I

I

I

(0111111) input for an 8-bit thermometer coded CSDAC

Iout

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Design Considerations for TC-CSDACs- Current sources laid out in common-centroid layout style to minimize impact of process variations
- A number of most significant bits (MSB’s) and least significant bits (LSB’s) are grouped within themselves to further reduce process variation impacts

ex: In a 16-bit converter, current sources indexed by consecutive number laid out on separate corners

m MSB’s are interpolated by n LSB’s where B, total number of bits, is m+n

Input to the TC-CSDAC is binary, hence binary to thermometer decoders used in the circuit

Proposed fault model can be applied to MSB and LSB parts separately

A Practical TC-CSDAC ArchitectureProcess-Aware Structural Soft Fault Model

- Process variations should not be mistaken as faults
- The proposed fault model: one current source might have an additional deviation from process variation effected value due to any modeled or un-modeled fault

probability

For each die, a current source will have a fixed value picked up from its probability density function, caused by process variations

Isource

I

Estimation of Process Variations

- Current sources are systematically correlated due to their close locations on die
- Current sources can be represented as a sum of independent components through a technique called Principal Component Analysis (PCA)
- Principal components corresponding to largest eigenvalues account for most of the variation
- Ratio of selected eigenvalues to all eigenvalues can be used to ensure a minimum variation

I : normalized current source variables

U : eigenvectors of correlation matrix

C : principal components

Estimation of Process Variations

- A reduced number of principal components, M<N, is equivalent to deleting some of the columns
- Then, M of these equations can be chosen to obtain an M equation-M unknown system
- The choice is made for consecutively indexed sources, as each source individually requires two measurements due to controllability restriction

Process Variation-Aware Test Nominals

- M sources are measured for each chip, U is calculated from correlation matrix, hence only C values are left to be determined
- Once C values are calculated, unmeasured N-M source I values can be calculated
- Hence, these steps provide process-variation aware nominal values for each current source using few measurements, as N>>M even for 98% variation

Acquiring Principal Components On-Chip

- Analog current is measured for up to principal component number of times; as low as ~ 6 measurements satisfactory to account for 98% variation
- No additional hardware is required to take these measurements, for ex. 6 consequent input codes, (0..0000000),(0..0000001),(0..0000011),.., (0..0111111), can be used to get these measurements

Correlation Model

- Output of a current source is spatially correlated to neighboring sources on layout as a result of silicon manufacturing steps
- A spatial distance2 correlation model is used
- According to the correlation model, the correlation starts from a number close to 1 and decreases towards 0 with distance between each pair of sources

Design for Test (DFT) Hardware

- One more decoder and some combinational gates added to the original decoder
- Similar modification done for row selection hardware
- test_sel=0 : original mode
- test_sel=1 : one column is selected using Ci inputs and setting row_sel=1

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Reduction of Diagnosis Time using DFT- Instead of exhaustively measuring current sources, particular groups of them are summed & measured
- This reduces the diagnosis time from quadratic to linear
- Process-variation aware nominal test points are used for each source to create variation aware nominals
- One row selected such that the sum of current sources within are deviating from the average of remaining row sums; similarly for columns

- Even a minor 20% deviational soft fault around process variation estimated values can be caught with ~100% efficiency!

Error Rates for Process Estimation

- Examination of normalized error in last column reveals that difference between real and estimated values are almost negligible using 6 principal components

Robustness for Increased Requirements

- Increasing bit requirements indicate detection of lower deviational faults due to averaging of non-faulty sources approaching the population mean

- A process variation aware DFT method is proposed
- Even minor soft faults can be caught with the proposed technique due to accounting of process variations
- A fast diagnosis procedure is proposed with reasonable addition of DFT HW
- The proposed technique becomes more robust for increased bit requirements

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