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8. Failure Rate Prediction Reliable System Design 2011 by: Amir M. Rahmani.

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Presentation on theme: "8. Failure Rate Prediction Reliable System Design 2011 by: Amir M. Rahmani."— Presentation transcript:

1 8. Failure Rate Prediction Reliable System Design 2011 by: Amir M. Rahmani

2 matlab1.ir Bathtub Curve Three phases of system lifetime – Infant mortality – Normal lifetime – Wear-out period Every product has a failure rate, λ which is the number of units failing per unit time.

3 Bathtub Curve (2) It is the manufacturer’s aim to ensure that product in the Infant mortality period does not get to the customer. This leaves a product with a Normal lifetime period during which failures occur randomly i.e. λ is constant, and finally a Wear-out period, usually beyond the products Normal lifetime, where λ is increasing. matlab1.ir

4 Usefulness of failure rate prediction - to assess whether reliability goals can be reached, - to identify potential design weaknesses, - to compare alternative designs, - to evaluate designs and to analyze life-cycle costs, - to provide data for system reliability and availability analysis, - to plan logistic support strategies, - to establish objectives for reliability tests. matlab1.ir

5 The failure rate prediction process - define the equipment to be analyzed - understand system by analyzing equipment structure - determine operational conditions: operating temperature, rated stress; - determine the actual electrical stresses for each component; - extract the reference failure rate for each component from the database; - sum up the component failure rates; - document the results and the assumptions. matlab1.ir

6 Failure Rate Calculation How do we can calculate failure rate? - Experimental system observation Needs to very long time Technology wear-out High cost - Failure prediction with standards Telcordia SR332/ Bellcore TR332 British Telecom HRD4 and HRD5 Siemens SN29500 (based on IEC 61709

7 COMPARISON OF FEATURES OF RELIABILITY PREDICTION METHODS matlab1.ir

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9 Failure Rate Prediction Mil-Hdbk-217F, 217F_1, 217F_2 Microprocessor, PAL, PLA, MOS device λ p = Π L Π Q (C 1 Π T + C 2 Π E ) failures/10 6 hours λ p is the part failure rate Π L represents the learning factor and is determined by the experience of the manufacturer Π Q is determined by the part quality C 1 is related to die complexity Π T is related to ambient temperature C 2 is related to the package type Π E is determined by the operating environment

10 matlab1.ir Failure Rate Prediction Π L : learning factor (section 5.10) Π L =2, for a new chip Π L =1 for product of a chip with high experience Π L = 0.01exp( *year) Π Q : quality factor, chips testing before selling (section 5.10) 1- A: Π Q =1, high quality military application (all chips will be tested) 2- B: Π Q =2, military application 3- C: Π Q =16, high quality commercial application (e.g. one of 1000 chips will be tested) 4- D: Π Q =150, standard components

11 matlab1.ir Failure Rate Prediction Π T : temperature factor (section 5.8) depends on: Device technology Packaging type Case temperature Power dissipation Π T = 0.1e -A(1/(Tj+273) – 1/298) A depends on: –Device technology –Packaging type T j = T c + θ jc * P –T c is case temperature –θ jc is temperature resistance between case and junctions –P is Max. of power dissipation

12 matlab1.ir Failure Rate Prediction C 1 : Die complexity (section 5.1) Microprocessor C 2 : Package type (section 5.9) for hermetic DIP C 2 = 2.8 * (N p ) 1.08 N p = Number of functional pin Π E : environment factor, (section 5.10), Table 3-2 Number of bitsBipolarCMOS Up to Up to Up to

13 matlab1.ir Failure Rate Prediction: Example What is the failure rate of MC6800 microprocessor? What is the failure rate for each gate? What is the MTTF? Function: 8-bit processor No. of gate:12667 Number of pins64 Technology:NCMOS Packaging:64 pin ceramic DIP hermetically Power dissipation:1.5 W Environment:room Case temperature:35 oc Power supply:+5 v Quality:commercial

14 matlab1.ir Failure Rate Prediction: Example Π L = 1, Π Q = 150 Π T = 0.1*e -5794(1/(35+15* )-1/298) = 0.68 C 1 = 0.06 C 2 = 2.8* (10 -4 )*(64) 1.08 = Π E = 0.38 λ CPU =Π L Π Q (C 1 Π T +C 2 Π E )= 0.75*10 -6 failures/hour λ gate = λ CPU /12667 = 0.395* failures/hour MTTF = 1/λ = 1/0.5*10 -6 = 2*10 6 ≈ 228 years

15 matlab1.ir Failure Rate Prediction Mil-Hdbk-217F Memories, SRAM, DRAM, xROM λ p = Π L Π Q (C 1 Π T + C 2 Π E +λ cyc ) failures/10 6 hours λ p is the part failure rate Π L represents the learning factor and is determined by the experience of the manufacturer Π Q is determined by the part quality C 1 is related to die complexity Π T is related to ambient temperature C 2 is related to the package type Π E is determined by the operating environment

16 matlab1.ir Hardware failure rates Ways of improving reliability of hardware – Decrease temperature – Decrease electrical stress – Reduce number of components or increase integration – Increase quality of components – Improve physical environment


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