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Yield and Variability Optimization of Integrated Circuits by J. C. Zhang, M. A. Styblinski (auth.)

By J. C. Zhang, M. A. Styblinski (auth.)

Traditionally, desktop Aided layout (CAD) instruments were used to create the nominal layout of an built-in circuit (IC), such that the circuit nominal reaction meets the specified functionality necessities. in fact, even if, a result of disturbances ofthe IC production technique, the particular performancesof the mass produced chips are various than these for the nominal layout. no matter if the producing technique have been tightly managed, in order that there have been little adaptations around the chips synthetic, the environmentalchanges (e. g. these oftemperature, offer voltages, and so on. ) could alsomakethe circuit performances range throughout the circuit existence span. Process-related functionality adaptations could lead on to low production yield, and unacceptable product caliber. For those purposes, statistical circuit layout thoughts are required to layout the circuit parameters, taking the statistical technique adaptations into consideration. This publication bargains with a few theoretical and useful facets of IC statistical layout, and emphasizes how they fluctuate from these for discrete circuits. It de­ scribes a spectrum of alternative statistical layout difficulties, akin to parametric yield optimization, generalized on-target layout, variability minimization, in line with­ formance tunning, and worst-case layout. the most emphasis of the presen­ tation is put on the foundations and useful strategies for functionality vari­ skill minimization. it really is was hoping that the ebook may possibly function an introductory reference fabric for varied teams of IC designers, and the methodologies defined can assist them increase the circuit caliber and manufacturability. The booklet containsseven chapters.

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For example: • From the geometrical point of view, placing nominal point at the center of the acceptability region will allow elements to have larger tolerances, or have high yield for fixed tolerances (if yield is less than 100%) . • The circuit performance y can be expressed as y(e) 17 = y(x + 0), so 18 CHAPTER 2 holds. t. the random variable (ji (associated with Xi), thus minimizing the performance variability due to (ji. Many of the previously proposed methods were explicitly or implicitly based on the above observations, including such methods as geometrical design centering [52] and sensitivity minimization [12, 13].

7. 8. Xl {3l +1 -1 +1 -1 +1 -1 +1 -1 Resolution V orthogonal array £8(2 7) X2 X1X2 X3 X1X3 X2 X 3 X1X2 X 3 {32 +1 +1 +1 +1 -1 -1 -1 -1 {3l2 +1 -1 +1 -1 -1 +1 -1 +1 {33 +1 +1 -1 -1 +1 +1 -1 -1 {3l3 +1 -1 -1 +1 +1 -1 -1 +1 {323 -1 -1 +1 +1 +1 +1 -1 +1 ({3l23) -1 +1 +1 +1 +1 -1 -1 -1 output Y Yl Y2 Y3 Y4 Ys Y6 Y7 Ys where It is shown that the estimated main effects using the main effect OA are now biased, and confounded with interactions. So, if the designer has to investigate interactions, an OA of higher resolution is required.

K need to be included in this model. The research on intra-die statistical modeling has been mostly focused on the computation of the mismatch variance. , on the separation distance between devices and device sizes. It has been observed that a small distance and a large size will lead to a small mismatch variance, hence large correlation coefficients. For example, if NI and N2 of the NMOS voltage divider are adjacent, the standard deviation of the VTH mismatch is small compared to its value if the transistors are separated by a large distance.

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