Being a relatively unknown but promising research area, Self-Formation is a way to increase the performance of products in the field of micro-electronics (semiconductors, including PV cells), nanotechnology, molecular electronics, biotechnology, and other fields. In this way, simpler manufacturing process can be achieved, with associated reduced manufacturing costs. The fundamental principle of Self-Formation is to generate structural growth processes as found in nature by applying smart software based on chaos theory, artificial intelligence and fuzzy logic principles.

Fundamental research, laboratory work and experiments, carried out over the last 25 years in Lithuania, have demonstrated a large potential of Self-Formation - in some cases, compared to traditional planar (2D) methods, a reduction of more than 50% in manufacturing costs was obtained. Nevertheless, application of Self-formation is still not widespread. This is mainly caused by stringent control of the Soviet Union in the past, who - not surprisingly - regarded Self-Formation as a strategically sensitive topic. Consequently, Self-Formation is only disseminated among Russian scientists, and publications are only scarce and almost exclusively in Russian. On top of that, the well-known political events in 1990 in the USSR caused that Self-Formation development suffered from lack of finance.

The term "self-formation" relates to the process of self-increasing of an object's complexity. Well-known planar technology for manufacturing of integrated circuits and electron devices based on lithographic processes is not the only possible way to manufacture electronic devices, integrated circuits or photovoltaic cells. Basically, planar technology - which is based on external formation - requires a defined sequence of interactions between a structured medium and the object being formed, in which both the configuration of the region to be formed and it position are defined by the structure of that medium and its alignment with the object. In contrast, self-formation controls the interaction between an object to be formed and a non-structured medium by the object structure itself, and this interaction induces the changes in the object's structure.

In other words, self-formation is object evolution in topological approximation. It can be regarded as more complicated cellular automata, where the set is not a priori defined, but is a function of the object's Euclidean surface, and is created by incremental step in time. Both planar and self-formation are based on topological dynamics. Common characteristics of both methods are parallel actions and their local positioning. In both cases, the objects evolve step by step (operates with discrete steps in time) and the main way to reach a result is to carry out the defined procedure.

However, there are differences between both methods. Cellular automata approximate a space by a two-dimensional plane with a set of chosen configuration cells, constant in time. Self-formation is based on a coplanar space comprising of parallel planes, where a set is formed by equidistant lines and trajectories. Any cell may have a different configuration. Furthermore, nature laws in cellular automata are defined by a set of rules, according to which any cell 'determines' its status for the next step (in time) depending on the status of their neighbouring cells. Interactions are only local. In self-formation, laws of evolution are defined by a matrix of interactions between the figures of internal and external points present in the Euclidean point or in a defined distance.

Another aspect is that the main processes in cellular automata methods are reversible processes keeping information as a fundamental characteristic of physics on the micro-level. In contrast, self-formation is based on irreversible processes, where information appears and disappears. This condition is necessary to form non-homeomorphous structures which in turn is required to enable the increase of structure complexity during evolutionary processes.

The fundamental difference between cellular automata and self-formation methods is that in cellular automata the initial structure and table of laws are known, but the final result is unknown, while - in contrast - in self-formation a final structure to be formed is defined. The main target is to choose (i) the simplest possible initial structure and (ii) a matrix of interactions that determine the growth process causing the defined final structure formation. It is important to note that the self-formation process can generate new figures that were initially non-existing in either the medium or the object.

Three types of self-formation of artificial planar systems can be used in manufacturing:

- self-formation based on a certain sequence of interactions between an object and non-structured media;
- development, which requires only a single non-structured medium;
- reproduction, which involves the development of an object generatied by initial objects.

The main goal of this project is to bring together fundamental and application-driven research to open novel and promising applications, providing excellent opportunities to young researchers to achieve scientific excellence in self-formation.

The proposed project foresees to cover following research topics:

1. Development of self-formation theory on artificial systems.
2. Application of self-formation technology, with the focus on three promising application areas with strong potential in the near future, and providing scientific challenges to young research talents:
   - Photovoltaics
   - Fuel Cells
   - Nanotechnology

Several experimental and technical challenges are to be met, in order to further develop Self-formation theory and to make it applicable to practical situations so that the above visions can materialise. The techniques that must be mastered are diverse and involve different fields of science and technology, such as physics, chemistry, cybernetics, synergetics, biology, informatics, solar cell technology, nanotechnology, and fuel cell technology etc. This provides not only a fundamental research challenge to young scientists, but the practical applicability will be an important fuel to their intrinsic motivation.