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# Strange New Worlds: shaping the future of the digital age

How do you build a billion-dollar microchip machine with zero room for error? You simulate its entire physical existence using Python digital twins before forging a single part.

- **Speakers:** [Andreas Kaldun](https://www.wearedevelopers.com/@andreas-kaldun)
- **Event:** World Congress 2023
- **Published:** September 21, 2023
- **Duration:** 30:29
- **URL:** https://www.wearedevelopers.com/videos/677-strange-new-worlds-shaping-the-future-of-the-digital-age

## Summary

Sustaining Moore's law and advancing microchip miniaturization requires optical manufacturing at unimaginable scales, often demanding picometer precision on massive extreme ultraviolet (EUV) lithography mirrors. Because manufacturing a single billion-dollar EUV machine leaves zero room for error, physical prototyping is no longer viable. Instead, engineering teams must validate hardware entirely through software, building exact digital twins of custom production machinery before a single physical component is forged.

To achieve this, developers build massive, domain-driven software ecosystems capable of simulating intricate physical properties. These digital replicas must account for light propagation, thermal noise, plasma physics, and even variations in Earth's gravitational pull when shipping mirrors globally. Anchored by a scientific Python stack, the architecture uses hierarchical abstraction layers that blend custom internal modules with open-source libraries, ensuring that physicists can express domain concepts naturally without compromising code quality or performance.

Bridging the gap between pure physics and scalable software engineering requires unique collaboration. Research software engineers bring architectural rigor to an environment where future physical problems—such as impending quantum tunneling effects at single-digit nanometer scales—are not yet fully understood. A critical architectural requirement is absolute reproducibility spanning decades; automated pipelines utilize centrally managed conda environments, git tracking, and timestamp-based loading to recreate the exact simulation state of a legacy optical system at the precise moment of its creation.

**Keywords:** microchip manufacturing, digital twin simulation, euv lithography, picometer precision optics, moore's law scaling, python scientific computing, domain-driven software architecture, software reproducibility, conda environment management, research software engineering, hardware-software integration, complex physics simulation, extreme precision manufacturing, timestamped environment reproduction

## Chapters

1. **Exponential growth in microchip transistor density** (00:03) — Keeping pace with exponential transistor growth requires continuous innovation in semiconductor manufacturing equipment.
1. **The step-by-step process of microchip manufacturing** (02:24) — Transforming raw silicon into functional microchips relies on lithography to project complex transistor patterns.
1. **Precision requirements for ultraviolet projection optics** (04:40) — Manufacturing microscopic transistor patterns demands extreme ultraviolet projection mirrors with picometer surface accuracy.
1. **Measuring sub-nanometer accuracy in optical manufacturing** (10:21) — Isolating picometer signals from environmental vibrations necessitates specialized measurement infrastructure and vacuum environments.
1. **Building digital twins for lithography machine simulation** (13:35) — Simulating physical properties and environmental factors eliminates the need for expensive hardware prototypes during system design.
1. **Bridging physics domain knowledge and software engineering** (17:10) — Translating complex physical equations into reliable production code requires collaboration between research scientists and software architects.
1. **Ensuring reproducibility for production software environments** (20:39) — Logging environments with specification files and git timestamps guarantees exact recreation of historical system simulations.
1. **The evolutionary progression of optical mechatronics systems** (22:05) — Advancing future semiconductor capabilities requires shifting from manual components to fully integrated custom hardware and software solutions.
1. **Integrating physical complexity into digital lithography simulations** (24:54) — Software developers enable continuous integration of new physical variables like plasma physics and gravitational pull into core simulation tools.

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