Fault Diagnosis Overview

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Fault Diagnosis Overview. David Lavo UC Santa Cruz January 13, 2005. Introduction: What is Fault Diagnosis? Components: What’s involved? Algorithm details: How does it work? Diagnosis in practice: How does it really work? Research: Why does (or doesn’t) it work? How should it work?.

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### Fault Diagnosis Overview

David Lavo

UC Santa Cruz

January 13, 2005

Introduction: What is Fault Diagnosis?

Components: What’s involved?

Algorithm details: How does it work?

Diagnosis in practice: How does it really work?

Research: Why does (or doesn’t) it work? How should it work?

Outline

Fault Diagnosis Overview

What is Fault Diagnosis?
• A guess as to what’s wrong with a malfunctioning circuit
• Narrows the search for physical root cause
• Makes inferences based on observed behavior
• Usually based on the logical operation of the circuit

Fault Diagnosis Overview

Defective Circuit

Observed

Behavior

Location or

Fault

Diagnosis

Diagnosis Algorithm

Physical Analysis

VLSI Fault Diagnosis (in One Slide)

Tests

Two Types of Diagnosis
• Circuit Partitioning (“Effect-Cause” Diagnosis)
• Identify fault-free or possibly-faulty portions
• Identify suspect components, logic blocks, interconnects
• Model-Based Diagnosis (“Cause-Effect” Diagnosis)
• Assume one or more specific fault models
• Compare behavior to fault simulations

Fault Diagnosis Overview

Circuit Partitioning
• Separate known-good portions of circuit from likely areas of failure
• Simplest method: identify failing flip-flops
• Tester can identify failing flops or outputs
• Input cone of logic is suspect
• Intersection of multiple cones is highly suspect
• Single clock pulse with scan can be used for sequential/functional fails

Fault Diagnosis Overview

aka Effect-Cause Diagnosis
• Reasoning based on observed behavior and expected (good-circuit) functions
• Commonly used at system and board-levels
• Tries to separate good and suspect areas
• Disadvantage: Not very precise, often gives no indication of defect mechanism

Fault Diagnosis Overview

Cause-Effect Diagnosis
• Start from possible causes (fault models), compare to observed effects
• A simulator is used to predict behavior of the circuit in the presence of various faults
• Match prediction(s) against observed behavior
• Advantage: Implicates a mechanism as well as a location
• Disadvantage: Can be fooled by unmodeled defects

Fault Diagnosis Overview

Behavior Signature

010001010100010101010 …

Defective Circuit

Comparison &

Conclusion

Diagnosis Algorithm

010100110000101010100 …

101000100001011101100 …

010100010100011101100 …

000111000101010011110 …

Fault Simulator

Candidate Signatures

Cause-Effect Diagnosis

Tests

Introduction: What is Fault Diagnosis?

Components: What’s involved?

Algorithm details: How does it work?

Diagnosis in practice: How does it really work?

Research: Why does (or doesn’t) it work? How should it work?

Outline

Fault Diagnosis Overview

Components of Fault Diagnosis
• Fault models
• Fault simulators
• Fault dictionaries
• Diagnosis algorithms

Fault Diagnosis Overview

Fault Models
• A fault model is an abstraction of a type of defect behavior
• A fault instance is the application of a model to a circuit wire, node, gate, etc.
• Used to create and evaluate test sets
• For diagnosis, they can be used to simulate and predict faulty behaviors

Fault Diagnosis Overview

Stuck-at Fault Model
• The most-used fault model (by far)
• Simple to simulate and enumerate
• Effective for testing, fault grading, and diagnosis of some defects
• Many defects are not well represented by the stuck-at model

Node A stuck-at 1:

0/1

A

0/1

1

B

(Fault-free/faultylogic values)

0

X

0

1

1

Y

1/0

1

Bridging Fault Model

• Shorts are a common defect type in CMOS
• Different bridging fault models have varying accuracy and precision, from simplistic to very sophisticated
• Difficult or impractical to enumerate

Nodes X and Y bridged:

Node X forces Y

to a value of 0

Some Diagnostic Fault Models

Gate Fault

Net Fault

Bridging Fault

Path Fault

Fault Simulators
• A fault simulator can simulate instances of a particular fault model
• Inputs:
• Circuit (netlist)
• Test set
• Faultlist (list of fault instances)
• Output: circuit response
• Usually, simulates the presence of a single fault instance (“single-fault assumption”)

Fault Diagnosis Overview

Fault Dictionaries
• A fault dictionary is a database of the simulated responses for all faults in faultlist
• Used by some diagnosis algorithms for convenience:
• Fast: no simulation at time of diagnosis
• Self-contained: netlist, simulator, and test set not needed after dictionary creation
• Can be very large, however!

Fault Diagnosis Overview

The Full-Response Dictionary
• For each fault ( f ), store the response to each test vector ( v )
• One bit per vector, pass ( 0 ) or fail ( 1 )
• For each vector, store the expected output response ( o )
• Total storage requirement: f  v  o bits

Fault Diagnosis Overview

The Pass-Fail Dictionary
• For each fault, store only the test vector responses
• One bit per vector, pass ( 0 ) or fail ( 1 )
• Total storage requirement: f  v bits
• Much smaller than full-response, and often practical for even very large circuits

Fault Diagnosis Overview

Dynamic Diagnosis
• Alternative to dictionary-based diagnosis
• Fault simulation is only done for certain faults, based on test results
• Only simulate faults in input cones of failing flip-flops/outputs
• Dictionary is eliminated, but requires complete netlist and test pattern file
• Used by most commercial ATPG tools: Mentor Fastscan, Synopsys, Cadence, etc.

Fault Diagnosis Overview

Introduction: What is Fault Diagnosis?

Components: What’s involved?

Algorithm details: How does it work?

Diagnosis in practice: How does it really work?

Research: Why does (or doesn’t) it work? How should it work?

Outline

Fault Diagnosis Overview

Algorithm Details
• Role of a diagnosis algorithm
• Scoring methods
• Types of diagnosis algorithms

Fault Diagnosis Overview

Diagnosis Algorithms
• Algorithms compare observed behavior to predicted behaviors
• An algorithm attempts to “explain” the observed failures with fault candidates
• The job of a diagnosis algorithm is to report the best fault candidate(s)
• “Best” is determined by scoring method

Fault Diagnosis Overview

Fault Candidate Scoring
• Two common scoring methods
• Match/mismatch points
• Fault candidate probability
• Other common scorings:
• Hamming distance
• Set intersection/overlap
• Nearest neighbor

Fault Diagnosis Overview

Match/mismatch Point Scoring
• Award points for matching observed failures
• Optionally deduct points for not predicting fails
• Nonprediction: A behavior not predicted by candidate
• Misprediction: A prediction not fulfilled by behavior
• Commercial tools (e.g. Fastscan) are usually biased to lowest nonprediction

Fault Diagnosis Overview

Probabilistic Scoring
• Probability score based on matches and mismatches and error assumptions
• Weights for non- and mis-prediction
• Different prediction probabilities for different fault candidates (bridges vs. stuck-at)
• Usually normalized so that total of all candidates equals 1.0
• UCSC method uses probabilities to compare stuck-at candidates to bridges in same diagnosis

Fault Diagnosis Overview

Types of Diagnosis Algorithms
• Stuck-at
• Most common, best supported by tools
• Surprisingly effective (~60% exact matches)
• Very fast
• IDDQ
• Orthogonal set of failing data
• Requires interpretation of tester results
• Not well supported by tools

Fault Diagnosis Overview

Types of Diagnosis Algorithms (Cont)
• Bridging-fault
• May better represent common CMOS faults
• More complicated fault model
• Biggest problem: candidate selection
• Other possible (future) directions:
• Functional fails
• Delay fails
• Parametric failures

Fault Diagnosis Overview

Introduction: What is Fault Diagnosis?

Components: What’s involved?

Algorithm details: How does it work?

Diagnosis in practice: How does it really work?

Research: Why does (or doesn’t) it work? How should it work?

Outline

Fault Diagnosis Overview

Diagnosis in Practice
• Using a diagnosis
• Translating the results: circuit navigation
• Evaluating diagnosis quality
• Commercial diagnosis tools

Fault Diagnosis Overview

Using a Diagnosis
• Fault diagnosis is used to aid physical inspection and root-cause identification
• Diagnosis output is logical, not physical:
• Abstract faults (such as stuck-at)
• Gates, ports (nodes), and nets
• No information about location or size
• Translation to physical location requires navigation of circuit

Fault Diagnosis Overview

• Netlist
• Examine RTL (Verilog/VHDL etc) for gates and data paths
• Schematic
• Symbolic view of gates and wires
• Layout/artwork
• Graphical view of metal lines, poly, vias, cell boundaries, etc.

Fault Diagnosis Overview

Circuit Netlist

module TOP (CLK, Reset, StartOut, SiReady, Rst_CntN, Up_DnN, Wr, SDin, Wr_RAM, Wr_Rreg, RAM_Addr, ATG_TESTMODE, BIST_TESTMODE, SDout, TwoOnes, OneOne, NoOnes, TwoZeros, OneZero, NoZeros);

input CLK;

inout Reset, StartOut, SiReady, Rst_CntN, Up_DnN, Wr, SDin, Wr_RAM;

inout ATG_TESTMODE;

inout BIST_TESTMODE;

inout SDout, OneZero, NoZeros;

inout TwoOnes, OneOne, NoOnes, TwoZeros, Wr_Rreg;

// Tie off cells

TLOW tielow1 (.Q(tielow));

THIGH tiehigh1 (.Q(tiehigh));

// Inverted CLK

wire CLK_N;

INVFF clkinv (.Q(CLK_N), .A(CLK));

.PDE(tielow),

.IEN(tielow), .I(StartOut_I), .SIGNAME(StartOut),

.INMODE(in_mode_avail), .TESTI(jumper001),

.TESTIEN(tiehigh), .SCANIN(jumper001),

.OUTMODE(out_mode_avail), .TESTO(tiehigh), .TESTOEN(tiehigh),

.O(tielow), .OEN(tiehigh));

• Either use text editor on netlist, or use browser function in simulator
• Browsers allow you to trace forward and backward and see logic values
• Can be used to view hierarchy and functional blocks
• Can be tedious

Fault Diagnosis Overview

• Either hand-drawn (from netlist navigation) or tool-generated gate symbols and wires
• Schematic tools in simulators also allow forward and backward traversal and display of logic values
• Used to verify fault propagation
• Does not reflect physical distances

Fault Diagnosis Overview

• Use routing/floorplanning tools to view artwork
• Can usually input cell or wire name and tool will highlight the object
• Useful for determining (x,y) values
• Also good for evaluating physical implications of a set of fault candidates
• Faults clustered in a small area are good

Fault Diagnosis Overview

Faults contained in small area: physical examination is possible

Fault Proximity
Evaluating a Diagnosis
• A diagnosis without one or a few strong (high-scoring) candidates is usually poor
• Can indicate:
• Multiple defects
• Unmodeled (complex) behavior
• Inappropriate algorithm
• If the diagnosis is poor, either try another algorithm or look for more data (failures)

Fault Diagnosis Overview

Evaluating a Diagnosis (cont)
• Many diagnoses (~60%) implicate a single stuck-at fault
• Usually a good sign, but you must consider equivalent faults
• Many defects can mimic a stuck-at fault, without being a short to Vdd or Gnd
• Consider nearby nodes also, if practical

Fault Diagnosis Overview

Dominance Bridging Fault

Strong inverter

FIB short

Weak inverter

Top candidate is stuck-at fault

on this node.

Candidate #2 is Best

Candidate #2

Candidate #1

Candidate #3

FIB short

Commercial Tool:Mentor Graphics
• ATPG tool: Fastscan
• Stuck-at diagnosis only
• No IDDQ capability
• Orders candidates by number of matched failures (biased to lowest non-prediction)
• Also has netlist & schematic browser
• Based on Waicukauski & Lindbloom (D&T‘89)

Fault Diagnosis Overview

Commercial Tool: Synopsys
• ATPG tool: TetraMAX
• J. Waicukauski moved to Synopsys after writing Fastscan
• Diagnosis capability unknown: assumed to be similar to Fastscan

Fault Diagnosis Overview

• ATGP tool: Encounter Test
• Test and diagnosis tools purchased from IBM
• IBM has had good diagnosis research, but Encounter’s capabilities are unknown
• Also of interest: Silicon Ensemble - routing tool
• Graphical artwork viewer
• Good for highlighting nets and cells based on diagnosis results
• Good for determining (x,y) and producing screen shots

Fault Diagnosis Overview

Introduction: What is Fault Diagnosis?

Components: What’s involved?

Algorithm details: How does it work?

Diagnosis in practice: How does it really work?

Research: Why does (or doesn’t) it work? How should it work?

Outline

Fault Diagnosis Overview

Prior Art
• Waicukauski & Lindbloom, IEEE Design & Test, Aug. ‘89
• Most widely-used algorithm for commercial tools
• Finds candidates to match individual tests, attempts to “explain” all failing tests
• Abramovici & Breuer, IEEE Trans. Computing, June ‘80
• Effect-cause diagnosis
• Permanent stuck-at fault assumption
• Aitken & Maxwell, HP Journal, Feb. ’95
• Analysis of relative importance of models vs. algorithms
• Lavo, Larrabee, et. Al., Proceedings of ITC ’98
• Probabilistic scoring
• Mixed-model diagnosis
• Bartenstein et. Al., Proceedings of ITC ’01
• SLAT: Single Location At-a-Time diagnosis
• Focus on matching per-vector results

Fault Diagnosis Overview

Prior Art (cont)
• Jee & Ferguson, Proceedings of ISTFA ’93
• Carafe – Inductive Fault Analysis (IFA)
• Examine circuit to determine likely failure locations
• Aitken, Proceedings of ITC ’95
• Using FIBs to insert defects
• Calibrate/evaluate diagnosis methods
• Henderson & Soden, Proceedings of ITC ’97
• Probabilistic physical failure analysis
• Nigh, Vallett, et. Al., Proceedings of ITC ’98
• Large-scale, multi-company SEMATECH experiment
• Failure analysis of timing and IDDQ fails

Fault Diagnosis Overview

Research Directions
• Complex defect behaviors
• Beyond stuck-at and 2-line bridges
• Intermittent faults
• Delay and timing-related defects
• Parametric & process-related defects
• Multiple simultaneous defects
• Is there a simple, inductive way to infer complex defects?

Fault Diagnosis Overview

Research Directions (cont)
• Diagnosibility
• What makes a particular circuit easy or hard to diagnose?
• What can we do to make diagnosis easier?
• Evaluation of diagnoses
• What makes a good diagnosis?
• Can we quantify our confidence in a diagnosis?

Fault Diagnosis Overview

Research Directions (cont)
• Integration with physical FA & yield improvement
• Can we incorporate process information?
• Can we produce a “physical diagnosis”?
• On-line (or even on-chip) diagnosis
• Commercial toolflow integration
• Can diagnosis tools use industry-standard data formats?
• Can commercial tools be scripted or programmed to do better diagnosis?

Fault Diagnosis Overview