SHF: Small: Bayesian Model Fusion: A Statistical Framework for Efficient Validation and Tuning of Complex Analog and Mixed Signal Circuits

自适应自愈是一种新兴的方法来对抗纳米级工艺变化的有害影响，以保持模拟和混合信号（AMS）电路的积极缩放。如今，每个可调AMS电路都成为一个大规模的复杂系统，可以随时间自适应地变化。与这种复杂系统的硅前验证和硅后调整相关联的过高成本是随着器件继续缩小以及关键工艺波动的相对幅度继续增长而日益严重的问题。因此，迫切需要开发新的统计方法，最大限度地减少验证和调整成本的纳米级AMS电路为未来的技术世代。该项目开发了一种新的统计框架，称为贝叶斯模型融合（BMF），旨在最大限度地减少模拟和/或测量成本的硅前验证和硅后调整的自愈AMS电路。所提出的BMF技术的动机是今天的AMS设计周期通常跨越多个阶段（例如，原理图设计、布局设计、第一次流片、第二次流片等）。关键思想是重用在早期阶段收集的仿真和/或测量数据，以便于在后期阶段所需的最少量数据的AMS电路的有效验证和调整。它提供了一个基本的基础设施，使下一代AMS设计的未来IC技术。拟议的项目提供了一个全新的AMS设计方法的基础上贝叶斯推理。预计它将在从消费电子到医疗仪器的广泛应用中为先进电路带来显着的性能改进。因此，成功发展拟议的BMF框架将对半导体行业产生短期和长期影响。此外，与该项目相结合的教育活动为大学生和工业工程师提供了许多独特的培训机会。它将大大改善教育基础设施，培养该领域的高质量研究人员和从业人员。

Adaptive self-healing is an emerging methodology to combat the deleterious effects of nanoscale process variations, to maintain the aggressive scaling of analog and mixed-signal (AMS) circuits. Nowadays, each tunable AMS circuit becomes a large-scale, complex system that can adaptively vary over time. The prohibitively high cost associated with pre-silicon validation and post-silicon tuning of such a complex system is a growing problem as devices continue to shrink and the relative magnitude of critical process fluctuations continues to grow. Hence, there is an immediate need to develop new statistical methodologies that minimize the validation and tuning cost of nanoscale AMS circuits for future technology generations. This project develops a novel statistical framework, referred to as Bayesian Model Fusion (BMF), that aims to minimize the simulation and/or measurement cost for both pre-silicon validation and post-silicon tuning of self-healing AMS circuits. The proposed BMF technique is motivated by the fact that today's AMS design cycle typically spans multiple stages (e.g., schematic design, layout design, first tape-out, second tape-out, etc). The key idea is to reuse the simulation and/or measurement data collected at an early stage to facilitate efficient validation and tuning of AMS circuits with a minimal amount of data required at the late stage. It provides a fundamental infrastructure that enables next-generation AMS design for future IC technology.The proposed project offers a radically new AMS design methodology based on Bayesian inference. It is expected to yield significant performance improvement for advanced electrical circuits in a broad range of applications, from consumer electronics to medical instruments. Hence, successful development of the proposed BMF framework will have both short-term and long-term impacts on the semiconductor industry. In addition, the education activities integrated with this project offer a number of unique training opportunities to both university students and industrial engineers. It will substantially improve the education infrastructure and generate high-quality researchers and practitioners in the field.