Fully embedded optical and electrical interconnections in flexible foils

This paper presents the development of a technology platform for the full integration of opto-electronic and electronic components, as well as optical interconnections in a flexible foil. A technology is developed to embed ultra thin (20 μ m) VCSEL's and Photodiodes in layers of optical transparent material. These layers are sandwiched in between two Polyimide layers to get a flexible foil with a final stack thickness of 150 μ m. Optical waveguides are structured by photolithography in the optical layers and pluggable mirror components couple the light from the embedded opto-electronics in and out of the waveguides. Besides optical links and optoelectronic components, electrical circuitry is also embedded by means of embedded copper tracks and thinned down Integrated Circuits (20 μ m). Optical connection towards the outer world is realized by U-groove passive alignment coupling of optical fibers with the embedded waveguides

Embedding of optical interconnections in flexible electronics

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Scalable genetic programming by gene-pool optimal mixing and input-space entropy-based building-block learning

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a recently introduced model-based EA that has been shown to be capable of outperforming state-of-the-art alternative EAs in terms of scalability when solving discrete optimization problems. One of the key aspects of GOMEA's success is a variation operator that is designed to extensively exploit linkage models by effectively combining partial solutions. Here, we bring the strengths of GOMEA to Genetic Programming (GP), introducing GP-GOMEA. Under the hypothesis of having little problem-specific knowledge, and in an effort to design easy-to-use EAs, GP-GOMEA requires no parameter specification. On a set of well-known benchmark problems we find that GP-GOMEA outperforms standard GP while being on par with more recently introduced, state-of-the-art EAs. We furthermore introduce Input-space Entropy-based Building-block Learning (IEBL), a novel approach to identifying and encapsulating relevant building blocks (subroutines) into new terminals and functions. On problems with an inherent degree of modularity, IEBL can contribute to compact solution representations, providing a large potential for knock-on effects in performance. On the difficult, but highly modular Even Parity problem, GP-GOMEA+IEBL obtains excellent scalability, solving the 14-bit instance in less than 1 hour

Embedded 45° micro-mirror for out-of-plane coupling in optical PCBs

We present an embedded 45° micro-mirror that can be used to couple light out-of-plane of the optical layer. The discrete micro-mirror is inserted in a micro-cavity into the optical layer. Loss measurements at receiver side show a mirror loss as low as 0.35dB

Elitist archiving for multi-objective evolutionary algorithms: To adapt or not to adapt

Objective-space discretization is a popular method to control the elitist archive size for evolutionary multi-objective optimization and avoid problems with convergence. By setting the level of discretization, the proximity and diversity of the Pareto approximation set can be controlled. This paper proposes an adaptive archiving strategy which is developed from a rigid-grid discretization mechanism. The main advantage of this strategy is that the practitioner just decides the desirable target size for the elitist archive while all the maintenance details are automatically handled. We compare the adaptive and rigid archiving strategies on the basis of a performance indicator that measures front quality, success rate, and running time. Experimental results confirm the competitiveness of the adaptive method while showing its advantages in terms of transparency and ease of use