The increased heat on an integrated circuit is a limiting factor for the performance and lifetime of a chip. The increased power density on chips resulted in increased heat and the formation of non-uniform hotspots. Earlier research has shown that the package design and layout of package components in flip-chip design can influence the thermal heat distribution on a chip. Especially the copper pillars play a role in the distribution of heat on a chip. These pillars can reduce the maximal temperature of a hotspot when placed correctly. Designing a pillarmap that adheres to manufacturing rules can be lengthy and complex. Therefore, pillarmaps are currently not optimised for thermal behaviour. The default strategy is to place them in a grid. This research aims to automatically optimise pillarmaps for thermal heat distribution with the use of artificial intelligence. Pillarmaps are optimised using the theoretical behaviour of heat in packages combined with Stochastic Hill Climbing and Squeaky Wheel Optimisation techniques. The layouts are automatically re-simulated in a finite elements simulation tool when needed. The optimisation strategies are tested on experimental setups with single and dual hotspots. The optimisation strategies are also evaluated on an actual product, but limitations on pillar placement reduced the effect of the optimisation techniques. Also, the influence of different pillar diameters on an optimisation strategy is discussed. The optimisation techniques can reduce hotspots and improve the heat distribution in a scenario with multiple hotspots. The success of the optimisation techniques is subject to the amount of freedom they have to change pillar locations and where the hotspots are on the chip.

Wisdom of the crowds is the idea that groups of people can collectively make wise decisions. Research suggests that these crowds can even outsmart experts. To gather the wisdom of the crowds, this project utilizes a prediction market. To successfully gather the wisdom of the crowds, a predictionmarket has to overcome serious challenges, such as gathering a large and active user base, and deciding on a fair initialmarket value. The main goal of the project is to create a prediction market that can overcome these challenges and successfully gather the wisdom of the crowds. Research has been done in the field of prediction markets. This process started with researching the theory behind prediction markets, the wisdom of the crowds. After that evaluating existing prediction markets and reviewing literature related to those markets was useful. Before and during the research phase, clear goals were set for the project, together with a clear set of requirements. These goals can be divided into: leveraging the wisdomof the crowd, solving problems associated with predictionmarkets and developing a product that is easily maintainable. The final product reaches the goals of the project and meets the requirements. The prediction market correctly aggregates the estimations of users on the market, and provides probabilities on real-world events. These probabilities are contained in the values on the market. The prediction markets solves the problems encountered on other prediction markets. The project makes use of gamification, an automated marketmaker and a reward system to correctly initialise market values. The system was thoroughly tested and developed with maintainability in mind.