Lam Research Recruiter Screen Prep
45 min recruiter screen | April 24, 2026 | Self-applied | Interviewer unknown
Part 1: Company Background (Know This Cold)
What Lam Does
Wafer fabrication equipment (WFE). Three product lines: etch (plasma/chemical material removal), deposition (thin film layering: CVD, ALD, PVD), clean (post-process residue removal). #1 globally in dry etch. Top-3 WFE vendor with Applied Materials and Tokyo Electron.
Lam vs. KLA
KLA inspects the chips (process control, metrology, defect detection). Lam builds the chips (etch, deposition, clean). Two halves of the fab ecosystem. You understand both sides.
Financials
- Q3 FY2026 (released April 22): $5.84B revenue (record, +24% YoY, 3rd consecutive record quarter)
- CY2025 full-year: $20.6B (+27% YoY)
- WFE CY2026 forecast: $140B (raised from $135B, management says "bias to upside")
- CSBG (service): $2.1B, first $2B+ quarter
AI Demand Impact
- HBM: every AI accelerator needs HBM stacks, 16-layer stacking requires more etch/deposition steps. Lam advanced packaging revenue growing >40%
- NAND: AI inference drives new demand vector, $40B in conversion spending pulled forward
- Logic/Foundry: GAA transistors at N2/N3 require dramatically more etch steps, foundry now 59% of systems revenue
Supply Chain Challenges (MUST KNOW)
- Long-lead-time components: subsystems with 12-18 month lead times
- China export controls: China was 43% of revenue, declining to <30%, ~$600M CY2026 headwind. Demand shifting from China to Taiwan, Korea, US simultaneously
- Clean room constraints: customer fabs can't expand fast enough, creating lumpy order patterns
- Supplier concentration: top 36 suppliers = 85% of direct spend
Manufacturing Footprint
- ~19,400 employees. 14 primary manufacturing locations
- Livermore, CA (where this role is): 2nd largest US manufacturing site
- Largest site globally: Batu Kawan, Malaysia
- Other major: Fremont CA (HQ), Tualatin/Sherwood OR, Ohio (Silfex, captive silicon supplier)
Kinaxis Maestro
Cloud-based supply chain orchestration platform. NOT an ERP. Sits on top of SAP/Oracle. Core differentiator: concurrent planning (all planning horizons calculated simultaneously in-memory, not batch-based like SAP IBP). Applied Materials already uses it company-wide.
Part 2: Self-Intro (~75 seconds)
I'm Regina Wu. I have over 10 years of supply chain experience across semiconductor capital equipment and consumer electronics.
I started my career in semiconductor at KLA, five and a half years. I managed production build plans for 2 divisions, $80 million in annual shipments, balancing demand against material and manufacturing constraints across 60 to 140 day production cycles. During COVID, I reset the division's entire build plan when 100% of tools were projected to push out, and got it down to 18% through scenario-based planning and manufacturing strategy redesign.
I moved to Amazon Lab126 specifically to build my end-to-end process skills in a high-volume, high-complexity environment. Over four years, I've owned capacity planning, production planning, and worldwide allocation across 6 manufacturing partners and 20 plus markets, 2 million units annually. I also built 30 plus SOPs and led our planning system migration, stress-testing the platform, resolving 15 critical issues, training the team.
What I'm looking for now is to bring that process discipline back to semiconductor. At KLA, I saw what the industry needed. At Amazon, I built the toolkit. This capacity planning role at Lam is where those two come together.
Part 3: Why Lam? (Pull then Push)
[Pull first]
Honestly, what drew me to this role is very specific. Lam is the dominant player in etch, and the AI demand cycle is driving an unprecedented equipment ramp. Your Q3 earnings just came out showing another record quarter, and the WFE forecast was raised to $140 billion. But what makes this capacity planning role so interesting is the complexity underneath that growth. You have the NAND conversion cycle, HBM stacking ramp, and the geographic demand shift from export controls all hitting the supply chain simultaneously. Planning capacity for that is not a back-office function; it's one of the hardest problems in the industry right now. That's the kind of problem I want to solve.
[Then push]
On the push side, I've had a great run at Amazon. It's genuinely one of the best supply chain organizations in the world, and I've learned a lot about building process at scale. But I'm a semiconductor person at heart. I spent five and a half years at KLA before Amazon. And the learning curve at Lab126 has flattened for me. I want to bring what I've built, the process discipline, the analytical rigor, the platform migration experience, back to an industry where I have real domain depth.
Part 4: Why You?
There are two things I bring that are hard to find in one person.
First, I have semiconductor domain depth. Five and a half years at KLA building capacity models for equipment with 60 to 140 day production cycles. I understand the supplier dynamics, the long lead times, and how capacity decisions cascade across the production schedule. I'm not starting from scratch on the industry.
Second, I'm a process builder who's proven at driving platform adoption. At Amazon, I co-led our planning system migration during a critical NPI period. I was the first person to stress-test the new platform, I identified and resolved 15 critical issues, trained 4 team members, and wrote the documentation. Your JD specifically mentions supporting Kinaxis enhancement and supply chain digital transformation. That's exactly what I just spent the last year doing at Amazon, just on a different platform.
Most candidates who have the semiconductor background haven't done platform migration at Amazon's pace. Most candidates who've done digital transformation don't have 5 years in semiconductor equipment. I have both.
Part 5: Kinaxis Gap
If asked "Do you have Kinaxis experience?":
I don't have direct Kinaxis experience, but I've worked with multiple planning systems across my career: SAP at KLA, Oracle at Genexus, and Amazon's proprietary planning platforms Opeth and DIDM. At Amazon, I co-led our planning system migration. I volunteered as the first user to stress-test the new system, identified and resolved 15 plus critical issues, trained 4 team members, and accelerated team adoption by about 3 weeks. All three NPI programs launched without platform-related delays.
So picking up new planning tools quickly is something I've demonstrated. And the JD mentions this role supports user testing for Kinaxis enhancements, which is exactly what I did at Amazon during our platform transition.
If asked to explain what Kinaxis does (show you researched):
I understand Kinaxis uses concurrent planning rather than the batch-based approach in traditional SAP. It runs demand, supply, and capacity scenarios simultaneously in memory, which means you can do what-if analysis in real time. For semiconductor equipment with 12 to 18 month lead times and lumpy demand patterns, that capability is critical. I know Applied Materials is also on Kinaxis, so it's becoming the industry standard for WFE capacity planning.
Part 6: Capacity Planning Stories
Each story: Context, What I Did, Result, Bridge to Lam.
Story 1: PLBM Capacity Model
Maps to JD: "maintain and improve commodity-specific capacity models"
Headline: "At Amazon I built capacity models analyzing factory efficiency, yield curves, shift capacity, and demand scenarios to secure a $4.95 million production commitment."
Quick Summary (~30 seconds):
Built capacity model from the ground up for 726K units. Analyzed factory-level factors: production efficiency, yield curves by variant, shift capacity, UPH by station. Modeled equipment requirements from BOM. Ran P80/P50/P20 demand scenarios. Cross-referenced with CM's projections, found where they were optimistic. Secured full approval. Model became the reference template. P50 accurate within 5%.
Structured STAR:
Context: Every major accessory program requires a Production Line Business Model before committing production spend. For our highest-volume program, I needed to secure approval for 726,000 units, a $4.95M commitment, plus $173,000 in CapEx.
What I did: Built the capacity model from the ground up. Analyzed factory-level factors: production efficiency, yield curves by product variant, shift capacity, and units per hour by station. Modeled equipment requirements based on BOM complexity. Ran three demand scenarios (P80/P50/P20) to stress-test whether the capacity investment was justified. Cross-referenced with the CM's own capacity projections and identified where their assumptions were optimistic.
Result: Secured full approval. The model became the reference template for subsequent PLBM reviews. P50 scenario proved accurate within 5% of actual demand.
Bridge: This maps directly to building and maintaining commodity-specific capacity models. At Lam the planning horizons are longer and there are more supplier variables, but the analytical framework is the same.
Full STAR Version (~50 seconds)
At Amazon, every major accessory program requires a Production Line Business Model before we commit production spend. For our highest-volume program, Brandy, I needed to secure approval for 726,000 units, a $4.95 million production commitment, plus $173,000 in CapEx for dedicated equipment.
I built the capacity model from the ground up. I analyzed factory-level factors: production efficiency, yield curves by product variant, shift capacity, and units per hour by station. Then I modeled equipment requirements based on BOM complexity. On the demand side, I ran three scenarios, P80, P50, and P20, to stress-test whether the capacity investment was justified across a range of demand outcomes. I cross-referenced the model with the CM's own capacity projections and identified where their assumptions were optimistic.
Secured full approval. The model became the reference template for subsequent PLBM reviews across other programs. The P50 scenario proved accurate within 5% of actual demand.
This is very similar to what the JD describes: building and maintaining commodity-specific capacity models to support build plans. The difference at Lam would be longer planning horizons and more supplier-level capacity variables, but the analytical framework is the same.
Story 2: KLA COVID Build Plan Reset
Maps to JD: "identify and mitigate supplier capacity risks"
Headline: "At KLA during COVID, I reset the entire division's build plan from 100% pushout down to 18% through scenario planning and manufacturing restructuring."
Quick Summary (~30 seconds):
2 divisions at KLA, $80M annual shipments. COVID hit: 100% tools projected to push out, 40% out of quarter. Lost 30% technicians, lead times doubled. Proposed three structural changes: multi-thread manufacturing pairing senior and junior techs, hedging long-lead materials earlier, three scenario-based build plans instead of one. 10+ revisions in 2 weeks with manufacturing, procurement, division GMs. Pushouts from 100% to 18%. Zero out of quarter. Became standard disruption playbook.
Structured STAR:
Context: Managing production build plans for 2 divisions at KLA, $80M annual shipments. COVID hit: lost 30% of experienced technicians, material lead times doubled or tripled. 100% of tools projected to push out, 40% out of the quarter entirely.
What I did: Proposed three structural changes. First, switch from single-thread to multi-thread manufacturing (pairing senior + junior techs). Second, hedge long-lead materials earlier than standard. Third, replace the single build plan with three scenario-based plans. Went through 10+ revisions in 2 weeks with manufacturing, procurement, and division GMs.
Result: Pushouts from 100% to 18%. Zero tools out of quarter. Scenario-based planning became the standard playbook for subsequent disruptions.
Bridge: Semiconductor equipment supply chains face long lead times and supplier concentration risk. At Lam, with 12 to 18 month subsystem lead times, the ability to scenario-plan and proactively mitigate is exactly what this role requires.
Full STAR Version (~55 seconds)
When COVID hit, I was managing production build plans for 2 divisions at KLA, $80 million in annual shipments. Semiconductor capital equipment, 60 to 140 day production cycles. My initial assessment: 100% of tools were projected to push out. 40% would miss the quarter entirely. We lost 30% of experienced technicians and material lead times doubled or tripled overnight.
I didn't accept the first assessment. I proposed three structural changes to manufacturing and senior leadership. First, switch from single-thread to multi-thread manufacturing, pairing senior technicians with junior ones so production could continue at reduced but non-zero throughput. Second, hedge long-lead materials much earlier than our standard process, because lead times had become unpredictable. Third, replace the single build plan with three scenario-based plans, so we could make decisions faster as the situation evolved. I went through 10 plus revisions in 2 weeks with manufacturing, procurement, and division GMs.
Pushouts went from 100% to 18%. Zero tools pushed out of the quarter. This approach, scenario-based planning with manufacturing flexibility, became the standard playbook for subsequent disruptions.
Semiconductor equipment supply chains face long lead times and supplier concentration risk. At Lam, with 12 to 18 month subsystem lead times and 85% of spend concentrated in 36 suppliers, the ability to scenario-plan and proactively mitigate is exactly what this role requires.
Story 3: Memory Shortage LTB
Maps to JD: "drive comprehensive supply and demand analysis, translate insight into actionable recommendations"
Headline: "I led a 244,000 unit last-time-buy decision under a memory shortage, building the demand forecast from first principles when the standard monthly cycle was too slow."
Quick Summary (~30 seconds):
Global memory shortage, monthly demand cycle too slow for weekly supply shifts. 244K unit last-time-buy for highest-volume Kindle cover. Attended device build meetings for real-time supply visibility. Cross-referenced device quantities with attach rates. Ran out-of-cycle demand review with marketing, built forecast from first principles. Fed parallel forecast chunks to manufacturing across 20+ channels to compress timeline. 244K units on ocean freight ahead of Q4. Revenue protected. Methodology became repeatable LTB playbook.
Structured STAR:
Context: Global memory shortage disrupted device supply. Standard demand planning runs monthly, but allocations shifted weekly/daily. Needed a last-time-buy decision on 244K units for our highest-volume program.
What I did: Attended device build meetings directly for real-time visibility. Cross-referenced device quantities against historical attach rates. Ran an out-of-cycle demand review with marketing, building the forecast from first principles. Fed preliminary forecasts to manufacturing in parallel chunks across 20+ channels so they could start processing while I finalized numbers.
Result: 244K units committed on ocean freight, arriving ahead of Q4 sales events. Revenue and in-stock protected. Methodology became a repeatable playbook.
Bridge: This kind of problem, where standard planning cycles can't keep pace with supply volatility, is what Kinaxis concurrent planning is designed to solve. Having done this manually, I can fully leverage the tool.
Full STAR Version (~60 seconds)
Earlier this year, a global memory shortage disrupted supply across multiple flagship devices at Amazon Lab126. The standard demand planning process runs monthly, but memory and PCB allocations from device teams were shifting weekly, sometimes daily. I needed to make a last-time-buy decision on 244,000 units for our highest-volume Kindle cover, and the monthly cycle was too slow.
I recognized the disconnect early. The prior month, I deliberately held off on the LTB because we were still outside lead time, and if device supply stabilized, I could preserve the standard forecast process. When it didn't stabilize, I pivoted. I attended device build meetings directly for real-time visibility into supply commits. I cross-referenced device quantities against historical attach rates. I ran an out-of-cycle demand review with marketing, building the forecast from first principles instead of waiting for the monthly refresh. And because the LTB spanned 20 plus marketplace channels, I fed preliminary forecasts to manufacturing in parallel chunks so they could start processing while I finalized the numbers.
244,000 units committed, all on ocean freight to arrive ahead of Q4 sales events. Revenue and in-stock protected for the largest program in the portfolio. The methodology became a repeatable playbook for future LTB decisions under upstream disruption.
This kind of problem, where standard planning cycles can't keep pace with supply volatility, is exactly what Kinaxis concurrent planning is designed to solve. Having done this manually at Amazon, I'd be able to fully leverage the tool's real-time scenario capabilities at Lam.
Story 4: Platform Migration
Maps to JD: "User Testing for Kinaxis enhancement or Supply Chain Digital Transformation"
Headline: "I co-led a planning platform migration at Amazon, volunteered as first stress-tester, resolved 15 critical issues, and trained the team."
Quick Summary (~25 seconds):
Migrated from legacy platform to two new systems, DIDM and Opeth, during three simultaneous NPI launches. No training or docs existed. Volunteered as first stress-tester on live programs. Found and documented 15+ critical issues over 2 months. Worked with platform team to resolve. Created onboarding docs, trained 4 team members, cut ramp-up by 3 weeks. Zero platform-related launch delays. Docs became standard onboarding package.
Structured STAR:
Context: Migrated from legacy planning platform to two new systems during a period with three NPI programs launching. No structured training or documentation.
What I did: Volunteered as first user to stress-test on live programs. Identified and documented 15+ critical issues. Worked with the platform team to prioritize and resolve. Created onboarding documentation and trained 4 team members, cutting ramp-up time by 3 weeks.
Result: All three NPI programs launched without platform-related delays. Documentation became the standard onboarding package.
Bridge: The JD specifically mentions supporting user testing for Kinaxis enhancements and documenting capacity planning processes. This is literally what I did at Amazon. Different platform, same discipline.
Full STAR Version (~50 seconds)
At Amazon, we migrated from our legacy planning platform to two new systems, DIDM and Opeth, during a period when I had three NPI programs launching simultaneously. There was no structured training or documentation. The platforms had known issues but nobody had mapped them systematically.
I volunteered to be the first user to stress-test both platforms on live programs. Over the course of 2 months, I identified and documented 15 plus critical issues, from data integration errors to workflow gaps. I worked directly with the platform team to prioritize and resolve them. I then created the onboarding documentation and trained 4 team members, cutting their ramp-up time by about 3 weeks. I accelerated the broader team's adoption timeline by the same margin.
All three NPI programs launched without any platform-related delays. The documentation and training materials I created became the standard onboarding package for subsequent users.
The JD specifically mentions supporting user testing for Kinaxis enhancements and documenting capacity planning business processes. This is literally what I just did at Amazon. Different platform, same discipline.
Story 5: Cross-Functional Leadership (Brandy Rework)
Maps to JD: "collaborate closely with cross-functional partners, influencing stakeholders"
Headline: "I coordinated a 262,000 unit rework across 6 sites on three continents with no formal authority, completing 58% faster than typical."
Quick Summary (~30 seconds):
Quality issue, 262K units across 6 fulfillment centers on three continents. No formal authority over any team: PM, quality, distribution, marketing, packaging, regional planning. Built clear data picture so everyone saw same reality. Daily milestones with clear ownership. Daily standups across time zones. Advocated dual-track approach to senior leadership: keep selling while reworking. Reduced scope from 508K to 262K. Completed 58% faster. Quarantine reduced 67% in 40 days. $250K+ saved. Distribution SOP now used team-wide.
Structured STAR:
Context: Quality issue required rework on 262K units across 6 fulfillment centers on three continents. No formal authority over any team involved: product management, quality engineering, distribution, marketing, packaging, regional planning.
What I did: Three things. First, built a clear data picture so every stakeholder saw the same reality: units at risk, cost exposure, timeline. Second, broke the problem into daily trackable milestones with clear ownership. Third, ran daily standups across time zones and pushed for a dual-track approach (keep selling while reworking), which I advocated to senior leadership, reducing scope from 508K to 262K units.
Result: Rework completed in 5 months (58% faster). Quarantine reduced 67% in 40 days. $250K+ saved. Distribution SOP now used for all new market expansions.
Bridge: This role sits at the intersection of commodity teams, procurement, network planners, inventory, and GIS. Driving alignment across stakeholders without direct authority is exactly what I do.
Full STAR Version (~55 seconds)
Our highest-volume program at Amazon, Brandy, had a quality issue requiring rework on 262,000 units across 6 fulfillment centers on three continents. I had no formal authority over any of the teams involved: product management, quality engineering, distribution, marketing, packaging, or the regional planning teams.
Three things. First, I built a clear data picture so every stakeholder saw the same reality: units at risk, cost exposure, timeline pressure. No ambiguity. Second, I broke the problem into daily trackable milestones with clear ownership, so there was never a question about who was responsible for what. Third, I ran daily standups across time zones and pushed for a dual-track approach: keep selling through normal channels while reworking quarantined units in parallel. I advocated for this to senior leadership, which reduced the rework scope from 508,000 to 262,000 units.
Rework completed in 5 months, 58% faster than comparable programs. Quarantine reduced 67% in 40 days. Over $250,000 saved. The distribution SOP I created during this process is now used for all new market expansions.
This role sits at the intersection of commodity teams, procurement, network planners, inventory, and GIS. Driving alignment across that many stakeholders without direct authority is exactly what I did on Brandy, and it's what I do daily.
Part 7: Story Deployment Guide
Recruiter screen 不太可能让你完整讲任何一个。先给 10 秒 headline，然后 "I can go deeper if you'd like."
| Recruiter asks... | Lead with headline from... |
|---|---|
| Capacity modeling experience? | Story 1 (PLBM) |
| Supply disruption / risk mitigation? | Story 2 (COVID reset) |
| Data-driven decision making / analytical skills? | Story 3 (Memory LTB) |
| Planning systems / digital transformation? | Story 4 (Platform migration) |
| Cross-functional collaboration / influencing? | Story 5 (Brandy rework) |
| Biggest challenge you've solved? | Story 2 (COVID) or Story 5 (Brandy) depending on audience interest |
| Process improvement? | Story 4 (Platform) or EOL review ($2.8M inventory reduction across 100+ programs) |
Part 8: Other Likely Questions
"Why are you leaving Amazon?"
I've had a great run at Amazon. It's one of the best supply chain organizations in the world, and I learned a lot about building process at scale. What I want next is to bring my capacity planning skills back to semiconductor, where I started. At KLA I spent five and a half years understanding the unique complexity of this industry. The learning curve at Lab126 has flattened, and this role at Lam combines my semiconductor foundation with the analytical and process skills I built at Amazon.
"What do you know about Lam Research?"
Lam is one of the top three WFE companies globally, dominant in etch, especially 3D NAND and conductor etch, and strong in deposition and clean. From my time at KLA, I understand Lam's position in the fab ecosystem. What's particularly exciting right now is the AI tailwind: HBM stacking, GAA transistors, NAND conversion all require more etch and deposition steps per wafer. Your Q3 earnings just came out yesterday showing another record quarter, and the WFE forecast was raised to $140 billion. The capacity planning challenge of matching supply to that kind of demand acceleration, while navigating the geographic shifts from export controls, is what drew me to this role.
"Salary expectations?"
I'm targeting competitive total compensation for a senior capacity planning role in the Bay Area semiconductor equipment space. I'd love to understand your range for this role first. I'm more focused on the scope of the work and the right fit.
"Do you have PowerBI experience?"
I use Python, SQL, and Advanced Excel for data analysis. I haven't used PowerBI specifically, but the analytical workflow is the same: pull data, transform, visualize for decision support. My PLBM capacity models and the EOL dashboard I built are examples of translating complex data into leadership-ready analysis.
"Where do you see yourself in 3-5 years?"
Deepening my expertise in semiconductor capacity planning. There's a long runway in this space, especially with the AI demand cycle, the geographic shifts, and the increasing complexity of advanced packaging. I want to become the go-to person for capacity strategy in this domain.
(Do NOT say you plan to move to a different function like GCM.)
Part 9: Questions to Ask
Pick 2-3:
-
"What are the biggest capacity planning challenges the team is facing right now? Is it more about the AI demand ramp, the geographic shifts from export controls, or something else?"
-
"How is the capacity planning team structured? Is it organized by commodity, product line, or supplier group?"
-
"What does the Kinaxis implementation maturity look like? Is the team still building out capability, or is the platform fully deployed?"
-
"What does the interview process look like from here?"
Part 10: Lessons Checklist (Scan 5 Minutes Before Interview)
| # | Rule | Why |
|---|---|---|
| 1 | Know Lam's supply constraints: long-lead components, China export controls, clean room limits, supplier concentration | NVIDIA fail: answered "demand" to supply constraint question |
| 2 | Don't mention other companies you're interviewing with | NVIDIA fail: disclosed AWS interest |
| 3 | Don't say career goal is to switch functions in 2-3 years | NVIDIA fail: said plan to move to GCM |
| 4 | No Amazon LP language (ownership, customer obsession, bias for action) | Lihong: other companies find it annoying |
| 5 | Show, don't label. Use concrete behavior, not abstract traits | Lihong: "Because I care" > "I have ownership" |
| 6 | Pull then Push for "Why Lam?" | Lihong: don't lead with career history, lead with what excites you about them |
| 7 | "I genuinely believe in this industry" is a red flag. Use specific facts | Lihong: every candidate says this |
| 8 | Translate Amazon jargon: FC = fulfillment center, CTB = clear-to-build, NPI = new product introduction | NVIDIA: interviewers don't know Amazon internal terms |
| 9 | If question is vague, clarify before answering | NVIDIA #1: misunderstood "advanced planning systems" |
| 10 | Self-intro under 90 seconds | NVIDIA #1: 3 min intro in 30 min screen was too long |
| 11 | End every answer with a bridge to Lam context | NVIDIA #1: all examples stayed in Amazon context |
| 12 | Headline first, details on request | Don't dump 60 seconds on a recruiter who might want to redirect |
Part 11: Logistics
| Question | Answer |
|---|---|
| Location | Livermore, On-site Flex (3+ days/week). San Jose to Livermore is ~45 min. Don't proactively raise commute concerns |
| Timeline | "Flexible for the right opportunity, ready to move quickly" |
| Sponsorship | No sponsorship needed |
| Currently employed | Yes, at Amazon Lab126 |
| Notice period | Know your Amazon notice period and state it if asked |
Quick Reference: Numbers
| Metric | Number |
|---|---|
| Amazon annual volume | 2M+ units, $80M revenue |
| Memory shortage LTB | 244K units, 20+ channels |
| Brandy program | 1.31M units, 20 IOGs, 39% of total |
| Brandy rework | 262K units, 6 FCs, 3 continents |
| Rework speed | 58% faster (5 mo vs 12 mo typical) |
| Rework savings | $250K+ |
| EOL inventory reduction | $2.8M rolling |
| PLBM approval | 726K units, $4.95M + $173K CapEx |
| SOPs created | 30+ |
| Platform issues resolved | 15+ |
| KLA tenure | 5.5 years, semi cap equipment |
| KLA annual shipments | $80M+, 2 divisions |
| KLA production cycles | 60-140 days |
| KLA COVID recovery | 100% to 18% pushout |
| KLA OTD improvement | ~0% to 60% |
| Manufacturing partners | 6 CMs |
| CM spend | $12M+ annually |