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Expected characteristics of the function fNorm x are these: The average jie of finding a mask defect within the area C approaches the probability that a mask defect exists on a point on the surface of the wafer. Use first sampling plan below arrow.
Such a probability process is known as the Poisson process. This probability is described by the binomial probability distribution fBin x, n, p. This is consistent with the fact that the geometric and exponential distributions are discrete and ji distributions, respectively IX The AQL plan measures the lot whose fraction defective is p1 as having the lowest acceptable quality level.
This is known as the multinomial distribution. MTBF known after the life of equipment is of no practical use. How the lot acceptance rate changes is illustrated by the operation characteristic curve OC curve in figure B.
The Gamma distribution is a failure probability density function if failure occurrence is considered to follow the Poisson process. If the amount of change in the characteristic value is found to be accelerated by thermal stress, in many cases the Arrhenius chemical reaction kinetics model can be applied to this phenomenon.
To serve as an example, consider the life L in equation IX In this case, the failure probability density function becomes equation IX for the Gamma distribution. This occurs in the case of a surge pulse withstand or mechanical impact test.
A.1 AQL Sampling Table
We can, however, make a generalization about the state function f X and the characteristic value X that defines the state. Regular versus Military Time. The level of degradation of the device jid be expressed as a function of the leakage current i. As long as there is no great variation jos the quality of this lot, the distribution of the life L as caused by the stress placed on the devices by the temperature T is expressed as a logarithmic normal distribution and should be analyzed as such.
As time t passes, the failure rate of these semiconductor devices changes. This model consists of m units connected in series, which i-th unit has ni devices connected in parallel. Such a system is known as a series system of redundancy of 0 figure B. Similarly, we cannot say that a cosmic ray will soon collide with a semiconductor device because there have been s collisions for a moment.
Number of pieces rejected. The possible test results are limited to either 1 “failure or defect”or 2 “no failure or acceptance,” with no possibility of such results as “pending decision” or “exception jus allowed. From one end of the wafer, each portion is inspected with a microscope.
This is based on equation IX Also, the probability that a device will not fail before time ti is the reliability function R ti. In this case as well, the failures of individual devices are taken to be mutually independent.
A.1 AQL Sampling Table
This process is explained in detail as follows. We deal with this issue in B. IX through IX, the Pascal and Gamma distributions are the discrete and continuous probability distributions for the same probability zz.
It is important to note that N no longer denotes the number of tests of Bernoulli trial due to the approximation of the binomial probability distribution to the Poisson distribution. This is referred to as upper 20 percent point. The Japan society of machinery water vapor tables new edition Note: We can find a relation between the Pascal distribution and the binomial distribution if we interpret the Pascal distribution as a Bernoulli trial comprising n – 1 tests with phenomenon 1 invariably occurring at the last n-th time after x – 1 times of phenomenon 1.
Noting the relation expressed by Eqs. If the original population is large, however, the fraction defective obtained through a sampling inspection can be approximated by the binomial distribution. Let us assume that a given lot of semiconductor devices are storage tested at temperature T. Wear-outfailures which almost suddenly outbreak after a certain period approximate the normal distribution.
For studying the change, in this model, probabilities of jie devices failing by the time t are uniformly p tand the probability that at least one of the n devices will fail is F t. This is explained as follows. That is, it is a non-maintainable component.
The lot is acccepted when failures are grater than Ac but less then Re. Math Statistics And Probability A.