FINETECH builds high-precision Die-Bonding, Packaging, and Assembly systems for the back-end of semiconductor manufacturing in Berlin. The machines place tiny to large components with sub-micrometer accuracy and support nearly all common joining technologies – ideal for cutting-edge R&D and early small series.

• Core strengths: modular platforms, process variety, extremely high positioning accuracy

• Proximity to customers: Engineering support from application to process – from the initial feasibility study to the transition to production

“Our systems are so flexible and precise that many deep-tech teams evaluate three or four bonding processes on the same machine – and can later go directly into small series.” — Travis Scott

Market cycle instead of long hype

Despite the AI boom, FINETECH feels the typical 3 to 5-year cycles of the semiconductor industry:

• Previous growth driver: optical transceivers for data centers

• Ascending: Quantum technologies and new photonics applications

• Current pace: rather consolidating than overheated – contrary to media perception

Globally built, digitally managed

Travis has spent years between Asia (Thailand, Taiwan, China, Korea), the USA, and Europe. Production and supply chains are globally interconnected; collaboration spans across time zones.
Since COVID, much has shifted from "on-site" to remote troubleshooting (Teams, TeamViewer) – ecologically sensible, cost-efficient, and family-friendly. Nevertheless, it remains: Hands-on on-site is irreplaceable for certain problems.

Where AI fits into precision manufacturing today

Travis does not see fully autonomous machines in the next 5–10 years. Instead, AI comes modular where it creates immediate value:

• Vision & Pattern Recognition: Alignment, marker detection, AOI/Defect Detection

• Automatic corrections & error handling: from image and process signals

• Internal knowledge agents: search & Q&A layer on technical know-how (process parameters, service histories, reasons for previous decisions)

• Onboarding booster: quick access to information for new engineers – without replacing human mentors

Misunderstanding #1: “AI everywhere”.
Reality: Often logic/automation is sufficient. Occam’s Razor applies – use AI where it is clearly superior.

Why many AI projects get stuck – and how to turn it around

It is not the technology, but the people who are the lever:

• Find champions: intrinsically curious, passionate colleagues drive pace and adoption.

• Use-case before technology: Define the problem → make data accessible → pragmatically iterate.

• Utilize APIs & modularity: Build systems that can interconnect, rather than monoliths.

• Data work first: Move away from Excel islands towards structured, findable knowledge repositories.

“The greatest accelerator is not a new model, but people in the company who are really on fire – they bring the pieces together and make it usable.” — Travis Scott

Undervalued opportunities for AI in the hardware cosmos

• AI-supported vision & motion control at sub-µm level (alignment, placement, AOI)

• Production data tracking (MES/SECS/GEM & Co.): Traceability across wafer, PCB, batch, and process chains – AI helps isolate recalls and anomalies faster

• Service assistance: Providing contextualized troubleshooting knowledge (parameters, tooling, “why” history)

Ten-year thesis: The optical internet + quantum as an amplifier

Travis locates us technologically where the internet stood in the 80s/90s – at the beginning of an S-curve:

• Electrons → Photons: Optics moves closer to GPU/CPU/Quantum die (co-packaging, silicon photonics)

• Advantages: higher speed, less loss/heat/power, longer distances

• Hurdles: Optical systems are more complicated & expensive – until economies of scale kick in

• Quantum building blocks: Quantum communication, QKD, sensing expand infrastructure and security

Result: An extended, optically driven internet that fundamentally accelerates computing and data paths – enabling new classes of devices and production processes.

Pragmatic takeaways for industrial enterprises

• Start small, ship fast: Begin with vision/AOI & knowledge search – clearly measurable ROI.

• Clean up data: Document and centralize metadata, versioning, process “why”.

• Bring AI where work is done: Buttons, workflows, correction loops – not just chat.

• People first: Foster internal AI champions; trainings pragmatic, tools self-explanatory.

• No AI badge: If classic automation suffices, it suffices.