CEO Interview with Aftkhar Aslam of yieldWerx

Why It Matters

Fragmented manufacturing data slows yield improvement and inflates costs; YieldWerx’s unified, scalable analytics directly boost profitability for high‑complexity semiconductor producers. Its digital‑thread approach is essential as the industry moves toward heterogeneous integration and AI‑enabled design‑to‑manufacturing cycles.

Key Takeaways

• YieldWerx unifies fragmented semiconductor manufacturing data.
• Platform handles billions of data points at scale.
• Focus on advanced packaging, photonics, MicroLED.
• Provides cross‑domain traceability and closed‑loop analytics.
• AI‑driven anomaly detection accelerates yield ramp.

Pulse Analysis

The semiconductor ecosystem is increasingly plagued by data silos spread across MES, test equipment, inspection tools, and ad‑hoc spreadsheets. This fragmentation forces engineers to spend weeks stitching datasets before any root‑cause analysis can begin, directly eroding yield and extending product cycles. YieldWerx addresses this choke point by establishing a unified digital thread that ingests, normalizes, and correlates data across wafer, module, and system levels, turning raw measurements into immediate, actionable intelligence for engineers and managers alike.

As advanced packaging, chiplet integration, and silicon photonics push device complexity to new heights, the volume of generated data has exploded into the billions of points per device. Traditional BI or wafer‑only tools cannot scale or understand the nuanced relationships between electrical and optical parameters. YieldWerx’s semiconductor‑native data model and extreme‑scale architecture enable real‑time cross‑domain analytics, while AI‑driven anomaly detection predicts yield loss before it manifests, helping manufacturers accelerate ramp‑up for cutting‑edge technologies such as MicroLED displays and high‑performance AI chips.

The competitive landscape is crowded with legacy yield tools, generic BI platforms, and bespoke in‑house solutions, yet none combine semiconductor‑specific data semantics, massive scalability, and closed‑loop decision support. YieldWerx differentiates by delivering a purpose‑built infrastructure layer that not only visualizes data but also drives corrective actions across design and manufacturing stages. This capability reduces time‑to‑root‑cause from weeks to days, cuts test escapes, and improves overall equipment effectiveness, positioning the company as a strategic partner for OEMs, OSATs, and equipment vendors navigating the next wave of heterogeneous integration.

CEO Interview with Aftkhar Aslam of yieldWerx

Key takeaways

Aftkhar Aslam is the Co‑Founder and Chief Executive Officer of yieldWerx and a semiconductor industry veteran with more than 30 years of experience spanning manufacturing, test engineering, yield management, IP strategy, and enterprise digital transformation.

Under his leadership, yieldWerx has become a trusted data and yield‑analytics platform supporting semiconductor companies across fab, assembly, test, advanced packaging, photonics, and AI‑driven device manufacturing. The platform enables organizations to unify fragmented manufacturing data into scalable, actionable yield intelligence.

Prior to founding yieldWerx, Aftkhar held senior leadership roles at Texas Instruments, where he served as Worldwide Director of Test & Yield Management Solutions & Director of Digital Transformation in the space of Design and Delivery systems and solutions across hardware and software.

He also served as a Director within Accenture’s Industry X (IX) practice, where he advised leading global technology organizations including Intel, GlobalFoundries, Qualcomm, Lam Research, Microsoft, STMicroelectronics, and Skyworks. His consulting work focused on bridging the Design‑to‑Manufacturing divide — architecting Digital Thread and Digital Twin strategies that connected product design, IP management, manufacturing execution, test, and enterprise systems into unified operational frameworks.

Aftkhar holds patents and possesses deep expertise in intellectual property management and protection. His experience spans semiconductor IP lifecycle governance, secure data architectures, and protecting high‑value design assets across complex global supply chains.

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Tell us about your company.

yieldWerx is a semiconductor‑focused data and enterprise yield‑analytics platform. We help manufacturers unify data across fab, assembly, test, inspection, and advanced packaging into a single environment where engineers can extract real insight — not just generate reports.

What makes us different is that we tend to operate where the problems are hardest. We work with highly specialized, niche products and manufacturing flows — whether that’s heterogeneous integration, chiplets, co‑packaged optics, MicroLED with billions of pixel‑level data points, or silicon photonics requiring optical and electrical correlation. These aren’t simple wafer‑yield problems; they’re multi‑domain, multi‑stage challenges that traditional tools struggle to handle.

We’re purpose‑built for semiconductor manufacturing at extreme scale and extreme complexity. Our platform is designed to manage unconventional data models, massive datasets, and deep traceability requirements without breaking performance or usability.

At a high level, we help companies move from fragmented data silos to a connected digital thread — accelerating yield learning, reducing ambiguity, and enabling smarter, faster engineering decisions in some of the industry’s most advanced and specialized product environments.

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What problems are you solving?

The biggest problem in semiconductor manufacturing today isn’t lack of data — it’s fragmentation.

Data lives in MES systems, testers, inspection tools, spreadsheets, home‑grown databases, and separate analytics platforms. Engineers spend enormous time manually stitching it together before they can even begin root‑cause analysis.

We solve that by unifying the data model and enabling cross‑domain correlation — electrical + optical, wafer + module, socket + silicon, defect + yield, and so on.

Another major problem is scale. Modern devices generate massive datasets. Traditional tools weren’t designed for billions of data points. Ultimately, we reduce the time from anomaly detection to root cause — and that directly impacts yield, cost, and time‑to‑market.

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What application areas are your strongest?

We’re strongest in environments where complexity is high and data volumes are extreme.

That includes:

• Advanced packaging (2.5D/3D, chiplets, CPO)

• Silicon photonics

• MicroLED and display technologies

• AI and high‑performance compute devices

• Automotive and high‑reliability semiconductor manufacturing

Anywhere there’s multi‑stage manufacturing with complex traceability requirements — that’s where we add the most value.

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What keeps your customers up at night?

Three things:

1. Yield ramp speed — especially for new technologies. Every week of delay is expensive.

2. Escapes or over‑kill at test — failing good parts or shipping marginal ones.

3. Lack of traceability when something goes wrong.

Customers worry about whether they truly understand where yield loss is originating — is it the wafer, the packaging step, the bonding process, the socket, the test program? If the answer takes weeks to figure out, that’s a problem. Our goal is to make that answer visible in hours or days.

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What does the competitive landscape look like and how do you differentiate?

There are traditional yield tools, BI tools, and home‑grown systems.

Traditional yield tools often focus on wafer‑level analysis but struggle with cross‑domain traceability.

BI tools are flexible but require heavy customization and don’t inherently understand semiconductor manufacturing.

We differentiate in three ways:

1. Semiconductor‑native data model — we understand wafers, panels, bonding, pixel maps, optical lanes, serialized modules.

2. Extreme scalability — billions of records without performance degradation.

3. Closed‑loop capability — we don’t just visualize data; we enable correlation across design and manufacturing stages to drive actionable decisions.

We’re not just another dashboard — we’re the infrastructure layer for yield intelligence.

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What new features or technology are you working on?

We’re expanding heavily into:

• Pixel‑level and device‑level analytics for MicroLED and advanced displays

• Optical + electrical unified analysis for photonics and CPO

• Advanced spatial analytics and pattern recognition

• AI‑assisted anomaly detection and predictive yield modeling from the start of design

• Deeper integration with test hardware and equipment for closed‑loop optimization

We’re also strengthening genealogy and digital‑thread capabilities to support next‑generation packaging and heterogeneous integration. The industry is moving toward system‑level understanding, not just wafer‑level — and that’s where we’re investing.

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How do customers normally engage with your company?

Most engagements start with a specific pain point — slow yield ramp, fragmented data, lack of traceability, or scaling a new technology.

We typically begin with a focused pilot or proof‑of‑value around a real manufacturing dataset. Once customers see how quickly we can unify and analyze their data, the engagement expands into a broader enterprise deployment.

We also work closely with equipment providers, OSATs, and ecosystem partners, because yield today is collaborative — not isolated.

At the end of the day, we’re a long‑term partner. Once we’re embedded in the manufacturing data flow, we become part of the operational backbone.