Yield-driven iterative robust circuit optimization algorithm
This paper proposes an equation-based multi-scenario iterative robust optimization methodology for analog/mixed-signal circuits. We show that due to local circuit performance monotonicity in random variations constraint maximization can be used to efficiently find critical constraints and worst-...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers
2010
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/58966 |
| Summary: | This paper proposes an equation-based multi-scenario iterative robust
optimization methodology for analog/mixed-signal circuits.
We show that due to local circuit performance monotonicity in random
variations constraint maximization can be used to efficiently
find critical constraints and worst-case scenarios of random process
variations and populate them into a multi-scenario optimization.
This algorithm scales gracefully with circuit size and is tested on
both two-stage and fully differential folded-cascode operational amplifiers
with a 90 nm predictive model. The improving yield-trends
are confirmed across process and random variations with Hspice
Monte-Carlo simulations. |
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