In my research project I am inter­ested in bio-inspired and resource-efficient concepts for neuro­mor­phic comput­ing. My goal is to realize these concepts in optical and electronic systems based on organic semicon­duc­tor materi­als and to describe their physi­cal foundations.

Why is our human brain capable of incred­i­bly complex tasks such as image, speech and motion recog­ni­tion while consum­ing only a few watts of power? Can we imitate the functional princi­ples of the human brain in order to make its capabil­i­ties techni­cally usable? Can these tasks also be imple­mented in resource efficient way using sustain­able materials?

One promis­ing approach is the direct use of physi­cal systems for neuro­mor­phic comput­ing. The innov­a­tive idea is to classify the data directly on a small and energy-efficient sensor chip made of biocom­pat­i­ble materi­als instead of using large data centers.

In fact, there are hydro­car­bon compounds that have the neces­sary semicon­duct­ing proper­ties. So-called organic electronic-ionic mixed conduc­tors, for example, are partic­u­larly excit­ing. Firstly, they are based on the same basic princi­ples as synapses in the human brain, namely the exchange of electronic and ionic charge carri­ers in a liquid environ­ment. Secondly, the switch­ing and storage proper­ties can be modified and controlled. Thirdly, they are sensi­tive to environ­men­tal influ­ences such as light, temper­a­ture, chemi­cal environ­ment, etc. Thus, they offer the poten­tial to simul­ta­ne­ously detect and classify signals. In the future, one could imagine even a direct inter­face with biolog­i­cal systems.