Hold failures spike after derate update. How do you separate real variation impact from setup scripting mistakes?
CLOSURE CHAIN
1. METRIC — name the failing report line (WNS, DRV, DRC count, IR %)
2. HYPOTHESIS — 2–3 likely causes ordered by probability
3. EXPERIMENT — one cheap check (corner, clock, report, map)
4. FIX — minimal physical or constraint change
5. REGRESSION — what you re-run and what must not regressAnalysis views are contracts between constraints, libraries, RC corners, and derate models; weak contracts distort closure priorities.
Section: Data and Libraries
Primary artifact: Applied derate matrix report
Downstream dependency: CTS tuning, hold fixing strategy, and signoff confidence hinge on accurate views.
Own Timing Views and OCV Tables as a release decision, not a page of notes. A senior PD engineer names the metric, the physical mechanism, the cross-team dependency, and the smallest evidence-producing experiment.
STAFF REVIEW MEMO — Data and Libraries / Timing Views and OCV Tables
1. Current state
- Failing / watched metric: Applied derate matrix report
- Database tag, corner/mode, tool version: <fill before review>
- Physical scope: block, hierarchy, macro region, clock domain, or net class
2. Root-cause hypothesis
- Most likely mechanism: <name physical or constraint mechanism>
- Competing hypothesis: <name the second plausible cause>
- Evidence still missing: <report/map/schematic/check>
3. Proposed action
- Minimal reversible fix: <physical, constraint, ECO, or methodology change>
- Expected improvement: <metric delta>
- Regression risk: Misapplied derates drive incorrect ECO priorities and can miss true silicon variation failures.
4. Regression and signoff
- Re-run: Applied derate matrix report
- Must not regress: CTS tuning, hold fixing strategy, and signoff confidence hinge on accurate views.
- Decision owner: PD ownermechanism — Mechanism
inputs-outputs — Inputs & Outputs
reports — Reports & Metrics
debug-playbook — Debug Playbook
worked-example — Worked Example
pitfalls — Pitfalls & Red Flags
interview — Interview Drills
checklist — Review Checklist
Master Timing Views and OCV Tables through reports, not GUI habit.
Interview performance is structured closure thinking under time pressure.
one-slide metric dashboard for the scenario
hypothesis list sorted by likelihood and cost
one report/map per experiment
regression list after the proposed fix
A strong answer to any scenario starts with the failing metric and signoff context. A weak answer starts with a tool command or random optimization knob.
If you are stuck, restate the metric and ask for corner/mode/stage.
If multiple failures exist, separate hard gates from tracked risks.
If pressured to waive, describe approval path and silicon risk.
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.
Lesson 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>Interview performance is structured closure thinking under time pressure. 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.