Optimization of Parametric Yield: A Tutorial

Stephen, W; Feldmann, P.; Krishna, K.

doi:10.1142/s0129156492000059

Cited by 30 publications

(20 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous work [9,10] uses joint probability density functions (PDFs) of the circuit performance metrics and poses the yield optimization problem as a maximization of a higher dimensional integral which are estimated using Monte Carlo simulations. However, these methods are difficult to apply in modern applications due to their high runtime and memory requirements with increases in statistical parameters.…”

mentioning

confidence: 99%

Statistical optimization of leakage power considering process variations using dual-Vth and sizing

Srivastava

Sylvester

Blaauw

2004

Proceedings of the 41st Annual Design Automation Conference

View full text Add to dashboard Cite

Abstracttiming analysis tools to replace standard deterministic static timing analyzers whereas [8,27] develop approaches for the statistical estimation of leakage power considering within-die and across-die variations.Increasing levels of process variability in sub-100nm CMOS design has become a critical concern for performance and power constraint designs. In this paper, we propose a new statistically aware Dual-Vt and sizing optimization that considers both the variability in performance and leakage of a design. While extensive work has been performed in the past on statistical analysis methods, circuit optimization is still largely performed using deterministic methods. We show in this paper that deterministic optimization quickly looses effectiveness for stringent performance and leakage constraints in designs with significant variability. We then propose a statistically aware dual-Vt and sizing algorithm where both delay constraints and sensitivity computations are performed in a statistical manner. We demonstrate that using this statistically aware optimization, leakage power can be reduced by 15-35% compared to traditional deterministic analysis. The improvements increase for strict delay constraints making statistical optimization especially important for high performance designs.However, very little work has been done on using statistical approaches to perform circuit optimization. Previous work [9,10] uses joint probability density functions (PDFs) of the circuit performance metrics and poses the yield optimization problem as a maximization of a higher dimensional integral which are estimated using Monte Carlo simulations. However, these methods are difficult to apply in modern applications due to their high runtime and memory requirements with increases in statistical parameters. Recent approaches to counter the impact of process variation have generally been limited to post-fabrication techniques. Forward and reverse body-biasing have been shown to improve yield and result in tighter distributions of circuit performance [11]. Reference [12] compares the approaches of adaptive body-bias and adaptive power supply to counter process variability. In [13], a simple circuit structure is used to automatically generate the ideal body-bias which is a function of process parameters and is ideal for a localized portion of the die. Alternatively, [14] proposes an optimization method to counter the effects of process variations. However this approach does not actually use statistical analysis but instead employs a heuristic to prevent a buildup of critical timing paths during the optimization.Categories and Subject Descriptors: B.6.3 Performance Analysis and Design Aids General Terms: Algorithms, performance, reliability Keywords: Leakage, variability, optimization Thus, we see that although a large amount of work is aimed towards countering the effects of process variations, there is only limited effort thus far in developing optimization approaches that consider these effects making intelligent decisions bas...

show abstract

mentioning

confidence: 99%

Statistical optimization of leakage power considering process variations using dual-Vth and sizing

Srivastava

Sylvester

Blaauw

2004

Proceedings of the 41st Annual Design Automation Conference

View full text Add to dashboard Cite

show abstract

“…Therefore the initial design produced by the optimization may be a bad starting point for the remainder of the design (a complete statistical IC design [6]). Current statistical IC design algorithms assume that the initial circuit meets all the specifications at the nominal process point.…”

Section: Infinite Programming Approach To Analog Circuit Synthesismentioning

confidence: 99%

“…Further details of the formulations can be found in [23], and similar formulations have been presented in [1] [5]. This infinite programming formulation looks at yield as a constraint, and device mismatch disturbance [6][29] can also be incorporated into this framework, as will be shown by our examples.…”

Section: Infinite Programming Approach To Analog Circuit Synthesismentioning

confidence: 99%

“…A simple conceptual algorithm begins with a and solves (6) to obtain (note that has only one constraint, and so can be solved using standard NLP algorithms). Then, is computed by solving ; if , it stops; otherwise it proceeds to solve (7) for a , etc.…”

Section: A Conceptual Infinite Programming Algorithmmentioning

confidence: 99%

“…Since then we have seen approaches to optimization-based nominal circuit design [24], and even analog circuit synthesis [4][11] [12][13] [18][21] [26]. We have also seen approaches to statistical IC design [6][8] [15] [33] and even some recent approaches that include operating ranges [1] [5]. The goal of analog circuit synthesis tools is to eliminate many of the circuit designer's most tedious tasks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Synthesis Of Manufacturable Analog Circuits

Mukherjee

Carley

Rutenbar

IEEE/ACM International Conference on Computer-Aided Design

View full text Add to dashboard Cite

Probabilistic design of systems with general distributions of parameters

Ponnambalam

Seifi

Vlach

2001

Circuit Theory & Apps

View full text Add to dashboard Cite

SUMMARYThis paper presents a new method for ÿnding optimal solutions of systems with design parameters which are random variables distributed with various general and possibly non-symmetrical distributions. A double-bounded density function is used to approximate the distributions. Speciÿcations may require tracking constraints in time domain and stability conditions in frequency domain. Using sensitivity information, the proposed method ÿrst ÿnds a linearized feasible region. Afterwards it attempts to place a tolerance box of the design parameters such that the region with higher yield lies in the feasible region. The yield is estimated by the joint cumulative density function over a portion of the tolerance box contained in the feasible region. Optimal designs are found for a fourth-order servomechanism and actual yields are evaluated by Monte-Carlo simulation.

show abstract

Optimization of Parametric Yield: A Tutorial

Cited by 30 publications

References 0 publications

Statistical optimization of leakage power considering process variations using dual-Vth and sizing

Statistical optimization of leakage power considering process variations using dual-Vth and sizing

Synthesis Of Manufacturable Analog Circuits

Probabilistic design of systems with general distributions of parameters

Contact Info

Product

Resources

About