Highlighting young scientists’ contribution to the HYBRAIN project – Interview with Dominik Ditz

Interview with Dominik Ditz, University of Heidelberg.

Can you introduce yourself?

I am Dominik Ditz, a Master Student at University of Heidelberg, Neuromorphic Quantumphotonics.

Can you share with us the story/reason that led you to decide to become a researcher?

My journey began as an electrician for systems and equipment, where I developed a fascination for
understanding the underlying physics of electronic circuits.

This curiosity led me to pursue a degree in engineering physics, specializing in optical technologies.

During an internship at the Max Planck Institute for the Science of Light, I was introduced to the field of metasurfaces, which ignited my passion for nanooptics.

My bachelor’s thesis at Karl-Franzens University further deepened my interest in integrated photonics
through work on grating couplers for advanced camera technologies.

These experiences solidified my desire to explore innovative technologies at the intersection of optics and electronics.

Now, as a master’s student in photonics specialising in nanooptics, I am thrilled to contribute to cutting-edge research in neuromorphic quantum photonics within projects like HYBRAIN.

What are the domains that your research is focusing on?

My research focuses on integrated photonics, particularly the development and optical weight programming of photonic convolution processors.

These systems use silicon crossbar arrays loaded with phase change materials (PCMs) to create nonvolatile matrix weights.

The state of each cell is defined by gradually changing its phase between amorphous and crystalline states using a pulsed femtosecond laser in an out-of-plane free-space setup. Programming it by modifing the exinction ratio of each individual PCM cell.

Why did you decide to focus on these domains, what do you like about them?

I chose this domain because it perfectly combines my background in electronics and optics with my passion for innovation.

The integration of photonics and PCMs offers transformative potential for AI and IoT applications by enabling efficient in-memory computations with unprecedented speed and energy efficiency.

I am particularly fascinated by how these technologies mimic brain-like processing, addressing
key challenges such as latency and power consumption in conventional computing architectures.

This field is a representative of the forefront of technological advancement combining novel hybrid – chip
architecutres with artifical intelegence applications.

About your work in HYBRAIN

What are you working on in HYBRAIN?

I am currently fine-tuning a free-space setup to optically switch and program PCM matrix weights out-of
plane using a pulsed femtosecond laser.

This involves optimizing laser parameters and experimental conditions to achieve precise control over phase transitions in PCMs.

By doing so, I aim to establish a reliable method for programming weights that can be integrated into photonic convolution processors.

What is the expected outcome/impact of your work?

The primary goal of my work is to deliver a repeatable and reliable method for out-of-plane PCM
switching/programming.

This approach provides an alternative to in-plane switching, offering larger extinction ratios and mitigating challenges such as an additional number of grating couplers and crossings required for in-plane methods.

These improvements are particularly significant for weights that only need to be programmed once, enhancing system performance and scalability.

What do you like about the HYBRAIN project?

What excites me most about HYBRAIN is its innovative approach integrating photonics, PCMs,
unconventional electronics and neural networks to address current limitations in AI hardware.

The project’s focus on enabling real-time data processing with low latency and energy consumption aligns perfectly with my interests in advancing edge computing technologies.

It represents a pioneering step toward brain inspired computing architectures.

More specifically, what do you like about working on HYBRAIN?

I value the interdisciplinary nature of HYBRAIN, which combines expertise from photonics, electronics,
materials science, and software engineering.

This collaborative environment allows me to learn from diverse perspectives while contributing my own skills toward creating novel hybrid computing architectures.

Your personal insights

What is the potential application for the HYBRAIN technology that excites you the most? Motivare your answer.

The application of HYBRAIN technology that excites me the most is its potential to enable efficient in
memory calculations for advanced AI applications directly on edge devices.

This capability could revolutionize IoT by allowing real-time data analysis without relying on cloud-based solutions. For example, smart wearables for health monitoring could provide real-time diagnostics and alerts.

Autonomous robots and vehicles would benefit from ultra-low latency decision-making while maintaining low power consumption, an essential feature for portable devices and machines.

In your opinion, why should AI and Edge Computing researchers pay attention to the HYBRAIN technology?

AI and Edge Computing organizations should pay attention because HYBRAIN addresses critical challenges such as energy efficiency, latency reduction, and scalability.

Its brain-inspired hybrid architecture combines ultrafast photonic processing with unconventional electronics, enabling high-performance AI even in small scale or low-power devices.

This innovation eliminates reliance on continuous internet connectivity while expanding AI’s reach into decentralized systems like wearables or remote sensors—key enablers for next generation IoT applications.

If HYBRAIN is successful in creating its technology, how do you think the AI and Edge Computing domains will change as a result of the uptake of HYBRAIN’s technology?

HYBRAIN’s success would redefine AI hardware by demonstrating how hybrid electronic-photonic systems can surpass traditional architectures in speed and energy efficiency.

This could lead to widespread adoption of edge computing solutions capable of real-time decision-making with minimal power consumption.

Applications such as wearable health devices, smart sensors for environmental monitoring, or advanced
robotics would benefit significantly from this shift, enabling more responsive systems while reducing
environmental impact through lower energy usage.

What would be your advice for young women wishing to pursue their passion for AI, Edge Computing and other STEM domains with a research career?

My best advice for young researchers is to immerse yourself early in hands-on research experiences within academic or industrial settings.

Practical involvement not only deepens your understanding but also helps you identify areas you are passionate about.

Considering that I’m still in the early stages of my career as a master’s student, I have found these experiences invaluable in shaping my research interests and career path.