W2001BP Datasheet, PDF (5/9 Pages) Keysight Technologies – GoldenGate RFIC Solutions

Making Designs More Robust
GoldenGate features a suite of automation tools
that enable design teams to quickly analyze and
diagnose problem areas early in the design cycle,
and fully optimize circuit performance. It also
tightly integrates easy-to-use tools such as multi-
dimensional sweeps, optimization and load-pull
analysis.
GoldenGate’s broad range of powerful, easy-
to-use statistical tools helps pinpoint problems
during the design phase. Advanced Monte
Carlo algorithms speed trials while reducing the
number required. Yield sensitivity histograms
help identify critical design components. This
information allows designers to make the design
adjustments necessary to improve manufacturing
yield. A Sensitivity analysis quickly allows insights
on what parameters most strongly affect critical
performances.
Advanced Analysis Overview
– Multi-dimensional sweeps with unmatched speed
and convergence
– Fast yield and mismatch analyses for DC, AC, SP,
SSNA, and CR with full contribution summary table
– Sensitivity analysis for CR, SSNA and DC analyses
including sensitivity summary table
– Extensive load-pull setup and plotting capabilities
– Advanced Monte Carlo sampling algorithms
– Latin Hypercube
– Hammersley Sequence Sampling
– Boundary Mode and Orthogonal Arrays
– Fast yield and mismatch analyses for DC, AC, SP,
SSNA, and CR
– Full contribution table
– Powerful optimization engine
– Digital state sweeping for operational mode
performance, calibration and control sequences
– Unique transistor-level integer-N PLL simulator
– Steady-state circuit characteristics including
deterministic noise
– Random jitter and noise with contributors
Figure 5.
The Choosing Analyses Form simplifies the setup of advanced analyses. The Task field provides access to a variety of
multiple-run simulations including Monte Carlo analysis. Fast Yield Contributor (FYC) analysis is a unique capability
which enables very fast computation of device-level contributions within Monte Carlo analysis.
Figure 6.
GoldenGate’s distribution plots help identify
components causing variation.
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