SIEMENS Configurable BISR Chain for Fast Repair Data Loading User Guide

June 9, 2024
SIEMENS

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SIEMENS Configurable BISR Chain for Fast Repair Data Loading

SIEMENS-Configurable-BISR-Chain-for-Fast-Repair-Data-Loading-
PRODUCT

Motivation

  • Today’s designs may have tens of thousands of memories with repair redundancy
  • It takes a long time to load the repair data serially during system power-up
  • Can we take advantage of the fact that very few of the memories actually need repair to significantly speed up the process?

Outline

  • The general memory repair system
  • Prior work
  • Configurable BISR chain repair system
  • Experimental results
  • Conclusions

The general memory repair system

SIEMENS-Configurable-BISR-Chain-for-
Fast-Repair-Data-Loading-FIG1

  • Dedicated repair register for each memory
  • Repair enable indicates that memory needs repair
  • Most repair registers contain only contain 0s and allow compression of repair information in fuse box

Prior work (repair sharing)

SIEMENS-Configurable-BISR-Chain-for-Fast-
Repair-Data-Loading-PRODUCT

  • Use same repair solution for several memories
  • Good results obtained for memories using row repair and memories behind a shared bus
  • Limited application for distributed memories using column repair
  • Potential yield loss

Prior work (memory bypass)

SIEMENS-Configurable-BISR-Chain-for-Fast-
Repair-Data-Loading-FIG2

  • Each repair register can be bypassed
  • Configuration chain loaded first to select repair registers to include in chain
  • Pipeline flop needed to avoid long asynchronous paths
  • Speedup limited to about 5X

Configurable BISR chain repair system

SIEMENS-Configurable-BISR-Chain-
for-Fast-Repair-Data-Loading-FIG3

  • Extend Devanathan’s idea to bypass several repair registers at a time
  • Bypassing longer segments reduces the overhead associated to the configuration chain

Segment selection circuit (SSC)

SIEMENS-Configurable-BISR-Chain-for-
Fast-Repair-Data-Loading-FIG4

  • Structure similar to Segment Insertion Bit (SIB) of IEEE 1687
    • Selects/bypasses associated chain segment
  • SSC has additional circuitry to identify segments that need repair
    • 1-detection logic

Active scan path with bypassed segmentSIEMENS-Configurable-BISR-Chain-
for-Fast-Repair-Data-Loading-FIG5

  • Left segment included in scan path because at least one memory needs repair
  • Right segment bypassed because none of the memories need repair
    • Segment input forced to 0

Active scan path (configuration chain selected)SIEMENS-Configurable-BISR-
Chain-for-Fast-Repair-Data-Loading-FIG6

  • Active scan path (configuration chain selected)

Repair data programming sequence

SIEMENS-Configurable-BISR-Chain-for-
Fast-Repair-Data-Loading-FIG7

Power-up sequence

SIEMENS-Configurable-BISR-Chain-for-Fast-Repair-Data-
Loading-FIG8

Partitioning algorithm considerations

  • Number of segments depends on a few factors
  • Most important one is defect density
    • High defect density requires shorter segments to reduce probability of having to include a segment
  • Segments of pre-existing IP blocks must be included as is
    • Not always possible to implement optimal segment size

Calculation of optimal segment size

  • The BISR Chain Shifting time T = Nrepair X L/Nseg + 2 X Nseg
    • L: the total length of the repair registers
    • Nseg: number of segments
    • Nrepair : number of segments requiring repair
  • To minimize T
    • ( Nrepair X L / Nseg + 2 X Nseg )′ = 0
  • : = /2
  • = /g

Repair data loading speedup factor (single repair)SIEMENS-Configurable-
BISR-Chain-for-Fast-Repair-Data-Loading-FIG9

Repair data loading speedup factor (two repairs)SIEMENS-Configurable-
BISR-Chain-for-Fast-Repair-Data-Loading-FIG10

Repair data loading cycles

(assumed vs actual number of repairs)SIEMENS-Configurable-BISR-Chain-for-
Fast-Repair-Data-Loading-FIG11

Conclusions

  • A configurable BISR chain repair system is proposed to speed up repair data loading during chip power-up
  • Experimental results show that number of clock cycles can be reduced by one to two orders of magnitude compared to previous methods

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