Setup/Hold Closure Playbook — Interview Drills

Interview drills

Practice aloud for Timing Closure → Setup/Hold Closure Playbook. Use METRIC → HYPOTHESIS → FIX → REGRESSION.

Explain Setup/Hold Closure Playbook to a hiring manager in 60 seconds.

[INT][PD][TOPIC]

Q: Explain Setup/Hold Closure Playbook to a hiring manager in 60 seconds.

A:
Run an ordered fix stack for setup and hold so each action has measurable benefit and bounded regression risk.

FOLLOW-UP TRAP: Tool list without mechanism.

What report proves Setup/Hold Closure Playbook is done?

[INT][PD][TOPIC]

Q: What report proves Setup/Hold Closure Playbook is done?

A:
Name report_timing (worst path clusters) + WNS/TNS trend and acceptance criteria.

FOLLOW-UP TRAP: No metric — only 'looks good'.

What breaks if Setup/Hold Closure Playbook is done poorly?

[INT][PD][TOPIC]

Q: What breaks if Setup/Hold Closure Playbook is done poorly?

A:
Late-stage random buffering that regresses hold or power

FOLLOW-UP TRAP: Only mentions runtime, not silicon risk.

10+ year interview answer bar

At senior/principal level, the interviewer is testing ownership judgment more than vocabulary. Answer Setup/Hold Closure Playbook through failure mode, evidence, tradeoff, and release decision.

You inherit a late-stage Setup/Hold Closure Playbook failure one week before release. What do you do in the first hour?

[INT][PD][STAFF]

Q: You inherit a late-stage Setup/Hold Closure Playbook failure one week before release. What do you do in the first hour?

A:
Freeze the database tag, name the failing metric (report_timing (worst path clusters) + WNS/TNS trend), confirm corner/mode/stage, cluster the issue, assign the first experiment, and publish a regression/owner plan before making broad tool changes.

FOLLOW-UP TRAP: Jumping directly to optimization knobs without preserving evidence.

When would you stop trying to improve Setup/Hold Closure Playbook and escalate?

[INT][PD][STAFF]

Q: When would you stop trying to improve Setup/Hold Closure Playbook and escalate?

A:
Escalate when the remaining risk crosses ownership boundaries, consumes shared margin, changes signed-off assumptions, or threatens ECO timing fixes. Bring exact report lines and options, not vague concern.

FOLLOW-UP TRAP: Escalating without data or continuing alone after a cross-team decision is needed.

Deep dive: how this shows up in real closure

Timing closure is a signoff matrix problem, not a single worst-path problem.

Reports and artifacts to inspect

• report_timing -max and -min with corner, mode, SI, and OCV enabled

• path group WNS/TNS: which clock domain is actually blocking signoff

• net delay percentage vs cell delay percentage

• exception audit: false paths, multicycle paths, generated clocks

Mini case study

A -90 ps setup path has 75% net delay and crosses a macro channel. Sizing the launch flop is weak. The stronger response is to layer-promote or shorten the route, then re-run hold at the fast corner.

Debug branches

• If net delay dominates, look for physical fixes before cell sizing.

• If cell delay dominates, consider VT swap, sizing, or RTL micro-architecture.

• If only one mode fails, inspect mode-specific SDC overlays and case analysis.

Senior review question

Ask yourself: what single report line would prove this page's concept is either passing or failing?

What changes at 10+ years

• 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.

Principal-level review bar

Deep subpage 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.

What excellent looks like

• 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.

Closure note template

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>

Tradeoffs a senior engineer must discuss

Technical tradeoff

Timing closure is a signoff matrix problem, not a single worst-path problem. Explain not only the preferred fix, but what margin or schedule you are spending to get it.

Cross-team tradeoff

• 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?

Leadership communication

"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>."

Key takeaways

• Always connect the concept back to a measurable signoff artifact.

• A fix is not complete until you can name the regression checks.

Common pitfalls

• Optimizing by habit instead of reading the current report.

• Forgetting that a local fix can regress timing, routing, power, or PV elsewhere.