Build placement decisions from measurable congestion and timing evidence, then hand off cleanly to CTS and routing.
Placement quality is judged by congestion stability, legal site use, and post-CTS survivability.
Use incremental, hypothesis-driven updates instead of wide random re-placement.
Track every placement intervention with before/after report deltas.
Every topic should end with a signoff decision, not only concept recall.
Every fix should state expected metric movement and likely regression surface.
Every open assumption should have an owner, tag, and review date.
Every recurring issue should become a methodology guardrail or checklist item.
global-detailed-placement/ — Global vs Detailed Placement
congestion-analysis-fixes/ — Congestion Analysis and Targeted Fixes
legalization-spacing-rules/ — Legalization and Spacing Rule Integrity
timing-driven-placement/ — Timing-Driven Placement Strategy
scan-chain-aware-placement/ — Scan-Chain-Aware Placement
hierarchy-physical-partitioning/ — Hierarchy and Physical Partitioning
placement-eco-and-incremental-flow/ — Placement ECO and Incremental Flow
placement-quality-metrics/ — Placement Quality Metrics and Readiness Gates
Placement closure is a data problem: maps, slack, displacement, and legalization stats must agree.
Placement converts floorplan assumptions into local density, timing, and congestion reality.
global placement congestion heatmap by layer and bin
density map: local cell density vs target utilization
pre-CTS timing: path wirelength, cell delay, net delay split
legalization summary: unplaced cells, overlaps, row/site errors
The block average utilization is 68%, but the router reports overflow near a soft macro. The correct read is that local density, not global utilization, is failing. Spread cells, adjust blockages, or revisit macro channels before detailed route.
If average utilization is fine but route overflows, inspect local bins and macro pin access.
If detailed placement fails, check hard blockages, row definitions, multi-height rows, and fence size.
If timing regresses after spreading cells, separate congestion-critical nets from timing-critical nets.
Ask yourself: what single report line would prove this page's concept is either passing or failing?
You are expected to predict what your fix can break before running it.
You should recognize when the issue is methodology, not one block's implementation.
You should communicate risk in tapeout language: owner, evidence, impact, mitigation, and decision date.
Hub pages in this course should be read like real closure review material. For a 10+ year PD engineer, the bar is not remembering terminology; it is making a release-quality decision under ambiguity.
Names the failing metric, corner/mode, database tag, and analysis switches before proposing a fix.
Separates data, constraint, physical, tool, and methodology root causes instead of treating all failures as optimization problems.
Chooses experiments by information gain and reversibility, not by habit.
States regression blast radius across timing, route, power, PV, DFT, package, and tapeout manifest.
Turns recurring failures into methodology guardrails, dashboards, or checklist items.
STAFF / PRINCIPAL CLOSURE NOTE
Context:
stage: <pre-CTS | post-CTS | post-route | post-fill | signoff>
tag: <database / netlist / SDC / library stack>
failing metric: <exact report line>
affected scope: <block / hierarchy / path group / power domain / region>
Hypotheses:
H1: <most likely physical or constraint mechanism>
H2: <competing explanation>
H3: <methodology or input-data issue>
Decision:
next experiment: <cheap check that can falsify H1>
fix candidate: <minimal reversible change>
rollback trigger: <metric that says the fix is wrong>
regression set: <timing / route / power / PV / DFT / package>
escalation owner: <team or reviewer>Placement converts floorplan assumptions into local density, timing, and congestion reality. Explain not only the preferred fix, but what margin or schedule you are spending to get it.
What must RTL, synthesis, CAD, STA, DFT, package, IP, or foundry agree to before this decision is final?
Which artifact becomes the source of truth after the decision: report, waiver, manifest, ECO script, or methodology deck?
What is the cost of being wrong: one rerun, ECO churn, mask risk, performance loss, or silicon escape?
"The current blocker is <metric> in <corner/mode/stage>. The leading cause is <mechanism>. I recommend <fix> because it is bounded and reversible. The regression surface is <domains>. If it fails, we escalate to <owner> with <evidence>."Always connect the concept back to a measurable signoff artifact.
A fix is not complete until you can name the regression checks.
Optimizing by habit instead of reading the current report.
Forgetting that a local fix can regress timing, routing, power, or PV elsewhere.