Layout generation remains a critical bottleneck in analog circuit design. It is especially distracting when re-using an existing design for a similar specification or when transferring a working design to a new technology. This paper presents a new methodology for layout generation of analog circuits that is based on a modular circuit design and a so-called "executable design flow description". This is created once manually and allows to describe the layout in a technology independent and parameterizable manner assuring a consistent view of circuit and layout design. Complex layouts can be created in negligible time, achieving an early involvement of layout effects in the circuit design. Furthermore, the parameterization of the design description allows simplified technology transfer and seamless access to sizing tools.
Abstract-Process variations increasingly challenge the manufacturability of advanced devices and the yield of integrated circuits. Technology computer-aided design (TCAD) has the potential to make key contributions to minimize this problem, by assessing the impact of certain variations on the device, circuit, and system. In this way, TCAD can provide the information necessary to decide on investments in the processing level or the adoption of a more variation tolerant process flow, device architecture, or design on circuit or chip level. In this first of two consecutive papers, sources of process variations and the state of the art of related simulation tools are reviewed. An approach for hierarchical simulation of process variations including their correlations is presented. The second paper, also published in this issue, presents examples of simulation results obtained with this methodology.
As feature sizes shrink, random fluctuations gain importance in semiconductor manufacturing and integrated circuit design. Therefore, statistical device variability has to be considered in circuit design and analysis to properly estimate their impact and avoid expensive over-design. Statistical MOSFET compact modeling is required to accurately capture marginal distributions of varying device parameters and to preserve their statistical correlations. Due to limited simulator capabilities, variables are often assumed to be normally distributed. Although correlations may be captured using Principal Component Analysis, such an assumption may be inaccurate. As an alternative, Nonlinear Power Models have been proposed. Since we see some limitations in this approach, we analyze whether the multivariate Generalized Lambda Distribution is an alternative for statistical device modeling. Applying both approaches to extracted statistical device parameters, we conclude that both methods do not differ significantly in accuracy, but the multivariate Generalized Lambda Distribution is more general and less computationally expensive
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