PoBO：机会约束成品率感知光子集成电路Optimization的多项式定界方法
PoBO: A Polynomial Bounding Method for Chance-Constrained Yield-Aware Optimization of Photonic ICs
摘要：
传统的成品率优化算法试图在工艺变化下最大化电路的成功率。这些方法通常获得高成品率，但达到的设计性能远离最优值。本文研究了另一种光子集成电路的成品率感知优化：我们将优化电路设计性能，同时确保高成品率要求。该问题最近被表述为一个机会约束优化问题，并利用统计矩将机会约束转化为更强的约束。这种转换减少了可行集，有时会导致过度保守的设计。为了解决这一根本性的挑战，本文提出了一个精心设计的多项式函数，称为最佳多项式亲属关系函数，以更准确地界定机会约束。我们通过放松独立性和凸性要求来修改现有的亲属关系函数，这适合于我们更一般的不确定性模型，并收紧了边界函数。所提出的方法，使一个全局最优值搜索的设计变量，通过多项式优化。我们用一个合成函数和两个光子集成电路设计基准验证了这种方法，表明我们的方法可以获得更好的设计性能，同时满足预定的成品率要求。许多其他先进的问题，产量感知优化和更一般的安全关键设计/控制可以解决基于这项工作在未来。
英文摘要：
Conventional yield optimization algorithms try to maximize the success rate of a circuit under process variations. These methods often obtain a high yield but reach a design performance that is far from the optimal value. This paper investigates an alternative yield-aware optimization for photonic ICs: we will optimize the circuit design performance while ensuring a high yield requirement. This problem was recently formulated as a chance-constrained optimization, and the chance constraint was converted to a stronger constraint with statistical moments. Such a conversion reduces the feasible set and sometimes leads to an over-conservative design. To address this fundamental challenge, this paper proposes a carefully designed polynomial function, called optimal polynomial kinship function, to bound the chance constraint more accurately. We modify existing kinship functions via relaxing the independence and convexity requirements, which fits our more general uncertainty modeling and tightens the bounding functions. The proposed method enables a global optimum search for the design variables via polynomial optimization. We validate this method with a synthetic function and two photonic IC design benchmarks, showing that our method can obtain better design performance while meeting a pre-specified yield requirement. Many other advanced problems of yield-aware optimization and more general safety-critical design/control can be solved based on this work in the future.
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