SI/PI Program Health CLOSED LOOP

Continuous learning loop status across DDR5 channel, SerDes lanes, PDN domains, and lab correlation. Every signal flows into an initiative, every initiative into tasks, every task back into the learning model.

Initiative Pipeline

Live · across program portfolio

Detected

14

Initiated

9

Planned

7

Executing

6

Closed

11

Loop Velocity

Last 90 days

−41%

debug cycle time vs. Rev C baseline

Functional Area Performance

Detection accuracy · First-pass rate · Cycle compression

⚙ Simulation Automation Policy

What IntelligenceIQ would run for you — without preempting engineer judgment

Auto-Run Eligible

4of 6 sims

Routine, deterministic, low design ambiguity

Engineer Hours Reclaimed

~14h / week

Hand-off, queueing & setup time eliminated

Idle-Farm Utilization

+28%

Off-peak windows now productive

Loop Latency ↓

−6h avg

Upstream commit → re-sim start

Why this matters. Engineers spend a measurable fraction of every week on routine sweeps that have no judgment content — regression checks, upstream-triggered re-sims after a commit, and coverage fills against archived stimuli. The Automation Policy moves those into IntelligenceIQ. Engineers keep the design-intent decisions; the platform handles the deterministic work. Every auto-run is bounded by guardrails and pauses for human review on any anomaly.

Trigger on upstream commit

Auto-run a sim when the only-changed input is committed (e.g. T-091 → T-092 sweep)

Fill idle farm capacity

Run regression and platform-rule sims when farm utilization drops below 50%

Off-peak batch window

Run queued auto-eligible sims between 22:00–06:00 PT

Auto-promote passing results

If all corners pass spec mask & trend matches prediction, advance task without human signoff

Human-in-the-loop: every auto-run pauses on anomaly & notifies the owner

★ AI-Recommended Next Simulations

Risk-prioritized queue · IntelligenceIQ ranks every variant

Queued

6

Total Variants

316

Est. Wall-clock

~18h

Coverage Gain

+35%

Click Why this score? on any row for the full weighted attribute breakdown

TrendIQ SENSE

Cross-domain anomaly surface across simulation, layout, lab and field telemetry. Click a signal to see Patterns, Correlations, Root Cause, Recommendations, and the tasks it spawns. AI proposes an Initiative Owner for every actionable signal.

Signal Feed

14 open · sorted by severity

Initiative Creation

From selected signal

Select a signal to compose an initiative

↓

DecisionIQ DECIDE

Initiative Owners decompose initiatives into executable tasks. AI suggests Task Owners with confidence scores and proposes a Closure Path — the sequenced route from problem to validated fix. Click an initiative to expand its task plan.

Active Initiatives

Default order · Initiative · Impact · Owner · Status

ActionIQ EXECUTE

Kanban-based execution. Toggle between the Initiative board (Open · In Progress · Completed) and the Task board (Backlog · Ready · In Progress · Blocked · Completed). Drag cards between columns. AI surfaces recommended workflows, automation opportunities, and similar past initiatives on each card.

Tip: drag to move · click an initiative to see its tasks · click a task to open detail

EffectivenessIQ LEARN

Measure whether each initiative achieved its intended outcome. Lab measurements close the loop — they confirm pre-validation predictions and refine the model for the next program. Human feedback annotations become training signal.

Debug Cycle Time

3.2 days

▼ 41% vs. Rev C

First-Pass Success

82 %

▲ +27pp vs. baseline

Respins Avoided

3 of 4 planned

~$4.8M NRE saved

Before / After — DDR5 Eye Margin (Channel B)

Closed-loop intervention

Before

0.18 UI

→

After

0.31 UI

Δ +72% eye opening · spec margin restored

Human Feedback — Outcome Annotation

Becomes training signal

Annotate the latest closed initiative. Your input refines the next prediction.

Decision Quality

✓ Correct call

↻ Partially correct

✗ Wrong path

⤵ Missed root cause

Cross-Program Learning Insights

Patterns the model has codified

📊 Insight 001 — DDR5 via-stub correlation

Programs N−2, N−1 and Rev C all surfaced via-stub-induced reflections at byte-lane 3. Topology guideline updated; next program inherits revised stub-length rule.

📊 Insight 002 — VRM substitution impact

Substituting Vendor-A VRM with Vendor-B raises PDN noise by ~18% on rails < 1.0V. Flag added to BOM substitution gate; PI re-sim auto-triggered on swap.

📊 Insight 003 — Decoupling density threshold

Below 0.7 µF/cm² on the 0.85V rail, transient droop crosses 5% threshold under burst write. Auto-recommendation seeded into ActionIQ for similar floorplans.