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Efficient On-Line Testing of FPGAs with Provable Diagnosabilities. Vinay Verma (Xilinx Inc. ) Shantanu Dutt (Univ. of Illinois at Chicago) Vishal Suthar (Univ. of Illinois at Chicago). Outline. Previous on-line testing methods

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Slide1 l.jpg

Efficient On-Line Testing of FPGAs with

Provable Diagnosabilities

Vinay Verma (Xilinx Inc. )

Shantanu Dutt (Univ. of Illinois at Chicago)

Vishal Suthar (Univ. of Illinois at Chicago)


Outline l.jpg
Outline

  • Previous on-line testing methods

  • Roving Tester (ROTE) & Bulilt-in Self Tester (BISTer) Concepts

  • Two new BISTer architectures

    • 1-diagnosable BISTer-1

    • 2-diagnosable BISTer-2

  • New fast functional testing and diagnosis: FAST-TAD

  • Simulation results (fault coverage and fault latency)

  • Conclusions


Previous on line testing methods l.jpg
Previous On-Line Testing Methods

  • On-line testing: Testing a (small) part of the FPGA while a circuit is executing on another part – increases system availability

  • Fault scanning technique of [Shnidman et al., IEEE Tr. VLSI’98] that is applicable to bus-based FPGAs

  • STAR technique of [Abramovici, et al., ITC’99] that uses a roving tester that tests part of the FPGA while the rest executes the application circuit.

    • Their group have presented several Built-in-Self-Testers (BISTers) with different diagnosabilities and complex adaptive diagnosis; e.g., [Abramovici, et al., ITW’00] – will be discussed later

    • Have also presented on-line BIST for interconnects [Stroud et al., ITW’01]


Slide4 l.jpg

Roving Tester (ROTE) with Built-in-Self-Testers (BISTers)

CUT

TPG

ORA

CUT

Syndrome

BISTer

  • Two column left spare for

  • ROTE; one for fault reconf.

  • ROTE roves across the FPGA

  • ROTE concept similar to STAR

  • at a high level

  • Differentiation:

  • BIST designs,

  • fault reconfig. & incr. re-routing

  • techniques

BISTer

ROTE

SPARECOLUMN

SPARECOLUMN

TPG - Test Pattern Generator

CUT - Cells Under Test

ORA - Output Response Anal.

CIRCUIT

CIRCUIT

CIRCUIT


Slide5 l.jpg

Definitions

k-diagnosability:

A testing technique is said to be k-diagnosableif in the

presence of any m ≤ k faulty components it can correctly

identify all m faulty components among the n ≥ k

components that it tests.

Detailed syndrome:

The detailed syndrome for a session is the 0/1 bit pattern

observed at the ORA output (0 => match, 1 => mismatch)

over all the test vectors of the TPG.

Gross syndrome:

A gross syndrome of a session is the overall pass/fail

(indicated as X/√) observation over all modes of operation

for that session. In other words, the gross syndrome of a

session is a X (fail) if the ORA output is 1 for any input test

vector and is a √ (pass), otherwise.

CUT

TPG

ORA

CUT

Syndrome

BISTer


Slide6 l.jpg

BISTer-0[M. Abramovici et. al., ITC ’99]

A

D

A

D

TPG

CUT

CUT

ORA

CUT

TPG

ORA

CUT

C

B

B

C

(S2)

(S1)

  • Exhaustive testing of CUTs

  • S1, S2, S3, S4 are four sessions

  • of testing in a BISTer tile

A

A

D

D

CUT

TPG

ORA

CUT

ORA

CUT

CUT

TPG

B

C

B

C

(S4)

(S3)

TPG - Test Pattern Generator

CUT - Cells Under Test

ORA - Output Response Analyser


Slide7 l.jpg

BISTer-0[M. Abramovici et. al., ITC ’99]

Theorem: BISTer-0 is zero-diagnosable.

Proof:

The same pair of PLBs are configured as

CUTs in two different sessions:

PLBs A and C in S2 and S4

PLBs B and D in S1 and S3.

When either PLB fails, the gross syndrome

will be identical in these sessions.

E.g. if A fails as a CUT only, then its gross

syndrome is identical to the gross syn. of

C failing as a CUT only. Hence we cannot

distinguish between faulty PLBs A and C.

Thus has a complex adaptive diagnosis phase


Slide8 l.jpg

Our BISTer-1 Architecture

A

B

CUT

TPG

TPG

ORA

A

B

C

CUT

ORA

D

CUT

C

CUT

D

Sess 

PLB 

S1

S2

S3

S4

A

TPG

ORA

CUT

CUT

B

CUT

TPG

ORA

CUT

C

CUT

CUT

TPG

ORA

D

ORA

CUT

CUT

TPG


Slide9 l.jpg

Our BISTer-1 Architecture

Sess 

PLB 

S1

S2

S3

S4

A

TPG

ORA

CUT

CUT

B

CUT

TPG

ORA

CUT

C

CUT

CUT

TPG

ORA

CUT

CUT

D

ORA

CUT

CUT

TPG

Each PLB is a CUT in 2 unique sessn’s

and a TPG in another unique session –

this serves to uniquely identify the faulty PLB which will have a X X √ in these sessions.

Theorem: BISTer-1 is 1-diagnosable


Slide10 l.jpg

BISTer-2 Architecture

B

A

CUT

TPG

C

F

ORA

2

ORA

1

Y1

Y2

E

D

TPG

CUT

Y1 – output of the ORA comparing CUTs

Y2 – output of the ORA comparing TPGs

Theorem: BISTer-2 is 1-diagnosable

Proof:

Gross syndrome corresponding to Y1 for each faulty PLB is unique.

E.g. Y1 is pass in section 2 only for faulty PLB A and no other PLB.

Gross syndrome corresponding to Y1

6 rotations => 6 sessions


Slide11 l.jpg

BISTer-2 Architecture (cont.)

dist. 3 pair

B

A

B

A

CUT

TPG

TPG

OR2

Y2

dist. 1 pair

C

F

C

F

TPG

CUT

OR2

OR1

Y1

Y2

E

D

E

D

CUT

OR1

Y1

TPG

CUT

(S2)

dist. 2

pair

B

A

OR1

CUT

Y1

F

C

TPG

CUT

E

D

OR2

TPG

Y2

(S6)

  • Theorem: BISTer-2 is 2-diagnosable under the assumptions:

  • 1. No fault masking for all detailed syndromes

  • 2. Faulty PLBs either uniformly all fail or all pass as TPG/ORA

  • Proof:

  • For the case faulty PLBs fail as TPG/ORA also, possible gross syndromes (GS) are: Y1Y2 = X √ and XX

  • Class 1: faulty pairs corresponding to GS= X √.

  • 3 Class 1 pairs: (CUT,CUT)2, (CUT,OR1)1 and (OR1,CUT)1

  • Class 2 includes remaining faulty pairs (GS=XX).

  • For session S1, Class 1 includes BD2, BC1 and CD1

(S1)

Class 1 pairs

Class 1 pairs

BC only Class 1 pair from S1

S1: GS =X X

=> Class 2 pairs

S1: GS =X √

=> BC/CD/BD

S2: GS =X X

=> BC/BD

S2: GS =X √

=> CD

S6: GS =X X

=> BD

In S1-S6 all the faulty pairs at dist. 1 & 2

will be in Class 1 and hence will be diag.

S6: GS =X √

=> BC

CD only Class 1 pair from S1

=> GS’s are distinct for all dist. 1 & 2 faulty pairs


Slide12 l.jpg

BISTer-2 Architecture (cont.)

A

B

OR2

TPG

Y2

C

F

CUT

TPG

E

D

OR1

CUT

Y1

(S3)

Three dist. 3 pairs

B

A

For faulty pairs at dist. 3, i.e., pairs AD, BE and CF,

G.S. of Y1Y2 = XX in all sessions.

Hence they don’t fall in Class 1 and hence are not distinguishable among themselves.

To distinguish these dist. 3 pairs we compare their detailed syndromes:

AD: dS1 = dS3 (T-C in both sess’s), dS4 = dS6 (C-T in both)

Similarly,

BE: dS1 = dS5, dS2 = dS4

CF: dS2 = dS6, dS3 = dS5

These pairs are uniquely diag. except for the case when

dS1 = dS3 = dS5 and dS2 = dS4 = dS6;

which is a very low probability event---e.g. requires 4

v. low prob. events of the type ds(CUT, TPG) = ds(TPG, CUT)

Thus all faulty pairs are diagnosable with high probability.

CUT

TPG

F

C

OR2

OR1

Y1

Y2

D

E

TPG

CUT

(S1)

The detailed syndrome for a session is the 0/1 bit pattern observed at the ORA output (0 => match, 1 => mismatch) over all the test vectors of the TPG.


Slide13 l.jpg

Fast-TAD: A Fast Functional Testing and Diagnosis

c1

c1

c2

c2

c7

c6

c5

ROTE

ROTE

ROTE

ROTE

  • In this methodology a PLB is tested only for specific functions (called operational functions) it will assume as the ROTE moves across the FPGA.

  • A PLB X is functionally-faulty (f-faulty) if faults in X produce incorrect outputs,

  • when X implements any of its operational functions.

  • Property:While roving the ROTE in an FPGA either without f-faults or with

  • reconfigured f-faults, a PLB X needs to implement at most 2 functions: its original function (when ROTE is in its initial position) and the fn. of the PLB two f-fault-free PLBs to its right.

Operational functions of c3

  • Advantages:

  • Faster T&D

  • >> yield

  • >> availab.

c7

c6

c3

c5

c4

fx1

fx2

fx3

fx4

c2

c1

c7

c6

c4

c3

c5

fx4

fx3

fx4

fx3

fx1

fx2

PLB in column c3 implements functions fx1 and fx3

as the ROTE moves across the FPGA.


Slide14 l.jpg

Diagnosis in Fast-TAD (overlaid on BISTer-1)

  • Each PLB is tested in its two operational fn.

  • A f-faulty PLB Q config. as a TPG will have

  • a GS of √while Q configured as a CUT &

  • performing its oper. functions will have GS

  • of X. In all other cases GS is either a √ or a X

Theorem: Fast-TAD using BISTer-1 is

1-diagnosable

  • In some cases, faults in A and C ( or B and D)

  • may not be distinguishable – a 2nd test reqd.

  • Require 10.t1 time versus 16.t1 if both CUTs

  • in a session are config. both their oper fns.


Slide15 l.jpg

Legend:

1

2

Center faulty PLB

Correlated faulty PLB

Non-faulty PLB

Simulation Environment

  • A 32 x 32 FPGA was simulated with 3-input 1-output PLBs.

  • Fast-TAD with BISTer-1 and STAR BISTer (enhancement of BISTer-0 with

  • 1-diagnosability) techniques were implemented on this FPGA.

  • The adaptive diagnosis phase of the STAR BISTer is very complex; we

  • have simulated only the fault detection and direct diagnosis phase of the

  • STAR BISTer (BISTer-1 has no adaptive diagnosis phase)

  • Two types of faults (with internal fault density up to 25%) were inserted:

  • 1. Randomly distributed faults with external faulty density up to 40%

  • 2. Clustered faults with cluster density up to 3%

Prob. of a fault around a “center” fault = k/d

(k=const, d=distance)


Slide16 l.jpg

Simulation of 3 x 2 STAR BISTer[M. Abramovici et, al., ITW ’00]

T – TPG, O – ORA, C – CUT

  • 1-diagnosable; it can diagnose 1 fault in a

  • 3 x 2 BISTer area (1 / 6).

  • Each BISTer consists of 3 TPGs, 2 CUTs

  • and 1 ORA – 6 sessions reqd.

  • STAR moves by 2 cols

  • Very complex adaptive diagnosis phase

Version of our 2 x 2 BISTer-1 w/ a 3-PLB TPG

  • # of TPG PLBs = ratio of inps/outps in PLB

  • => 3 TPGs for testing 3-inp1-outp PLBs

  • 2x3 BISTer-1: 3 TPGs, 2 CUTs & 1 ORA

  • Basically two partially overlapped basic 2x2 BISTer-1’s – 8 sessions reqd.

  • ROTE moves by 2 cols

  • Result: Can diagnose up to 1 fault in every alt. col of a 2-row FPGA subarray – diagnosability is thus 1 / 4 approaching that of ideal Bister-1’s


Slide17 l.jpg

Clustered faults with k = 0.5 in

The three values of fault density in the plot correspond to cluster densities of 1%, 2% and 3% respectively.

Results:

Fault Coverage v/s Fault Density

Randomly distributed faults



Slide19 l.jpg

Conclusions

  • Developed a 1-diag. (1 of 4) BISTer

  • Developed (for the 1st time) a 2-diag. (2 of 6) – w/ high prob. -- BISTer

  • Developed (for the 1st time)functional T&D: tests PLBs in only 2 funcs that they will perform; prev. methods performed exhaust testing

  • Fast-TAD w/ BISTer-1 has the samediagnosability (1 of 4) for f-faults

  • Our methodsdo not require adaptive diagnosis;previous techniques have complex adaptive diag. mechanisms

  • Simulation results forFast-TAD w/ BISTer-1:

  • fault coverages of 96% & 92 % at fault densities of 10% & 20% resp.

  • The previous bestSTAR-2x3-BISTer (non-adaptive version):coverages of 74% & 46% at these densities

  • Much lower fault latencyof Fast-TAD w/ BISTer-1 compared to that of the STAR-3x2-BISter

  • Its high fault coverage at high flt. densities and low fault latency should proveuseful for testing and diagnosing emerging tech. FPGAs (<= 90 nm, nanotechnology)that are expected to have high fault densities



Slide21 l.jpg

BISTer-2 architecture

B

A

CUT

TPG

C

F

ORA

2

ORA

1

Y1

Y2

E

D

TPG

CUT

Y1 – output of the ORA comparing CUTs

Y2 – output of the ORA comparing TPGs

Theorem: BISTer-2 is 1-diagnosable

Proof:

Gross syndrome corresponding to Y1 for each faulty PLB is unique.

E.g. Y1 is pass in section 2 only for faulty PLB A and no other PLB.

OR1 => ORA 1 (Y1)

OR2 => ORA 2 (Y2)

Gross syndrome corresponding to Y1


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