An evolutionary algorithm is presented for the automated calibration of the continuous cellular automaton for the simulation of isotropic and anisotropic wet chemical etching of silicon in as many as 31 widely different and technologically relevant etchants, including KOH, KOH+IPA, TMAH and TMAH+Triton, in various concentrations and temperatures. Based on state-of-the-art evolutionary operators, we implement a robust algorithm for the simultaneous optimization of roughly 150 microscopic removal rates based on the minimization of a cost function with four quantitative error measures, including (i) the error between simulated and experimental macroscopic etch rates for numerous surface orientations all over the unit sphere, (ii) the error due to underetching asymmetries and floor corrugation features observed in simulated silicon samples masked using a circular pattern, (iii) the error associated with departures from a step-flow-based hierarchy in the values of the microscopic removal rates, and (iv) the error associated with deviations from a step-flow-based clustering of the microscopic removal rates. For the first time, we present the calibration and successful simulation of two technologically relevant CMOS compatible etchants, namely TMAH and, especially, TMAH+Triton, providing several comparisons between simulated and experimental MEMS structures based on multi-step etching in these etchants.

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Presently, dynamic surface-based models are required to contain increasingly larger numbers of points and to propagate them over longer time periods. For large numbers of surface points, the octree data structure can be used as a balance between low memory occupation and relatively rapid access to the stored data. For evolution rules that depend on neighborhood states, extended simulation periods can be obtained by using simplified atomistic propagation models, such as the Cellular Automata (CA). This method, however, has an intrinsic parallel updating nature and the corresponding simulations are highly inefficient when performed on classical Central Processing Units (CPUs), which are designed for the sequential execution of tasks. In this paper, a series of guidelines is presented for the efficient adaptation of octree-based, CA simulations of complex, evolving surfaces into massively parallel computing hardware. A Graphics Processing Unit (GPU) is used as a cost-efficient example of the parallel architectures. For the actual simulations, we consider the surface propagation during anisotropic wet chemical etching of silicon as a computationally challenging process with a wide-spread use in microengineering applications. A continuous CA model that is intrinsically parallel in nature is used for the time evolution. Our study strongly indicates that parallel computations of dynamically evolving surfaces simulated using CA methods are significantly benefited by the incorporation of octrees as support data structures, substantially decreasing the overall computational time and memory usage.

Aims:  To study the ability of multi‐layer perceptron artificial neural networks (MLP‐ANN) and radial‐basis function networks (RBFNs) to predict ochratoxin A (OTA) concentration over time in grape‐based cultures of Aspergillus carbonarius under different conditions of temperature, water activity (aw) and sub‐inhibitory doses of the fungicide carbendazim. Methods and Results:  A strain of A. carbonarius was cultured in a red grape juice‐based medium. The input variables to the network were temperature (20–28°C), aw (0·94–0·98), carbendazim level (0–450 ng ml−1) and time (3–15 days after the lag phase). The output of the ANNs was OTA level determined by liquid chromatography. Three algorithms were comparatively tested for MLP. The lowest error was obtained by MLP without validation. Performance decreased when hold‐out validation was accomplished but the risk of over‐fitting is also lower. The best MLP architecture was determined. RBFNs provided similar performances but a substantially higher number of hidden nodes were needed. Conclusions:  ANNs are useful to predict OTA level in grape juice cultures of A. carbonarius over a range of aw, temperature and carbendazim doses. Significance and Impact of the Study:  This is a pioneering study on the application of ANNs to forecast OTA accumulation in food based substrates. These models can be similarly applied to other mycotoxins and fungal species.

The NEutron Detector Array (NEDA) project aims at the construction of a new highefficiency compact neutron detector array to be coupled with large γ-ray arrays such as AGATA. The application of NEDA ranges from its use as selective neutron multiplicity filter for fusionevaporation reaction to a large solid angle neutron tagging device. In the present work, possible configurations for the NEDA coupled with the Neutron Wall for the early implementation with AGATA has been simulated, using Monte Carlo techniques, in order to evaluate their performance figures. The goal of this early NEDA implementation is to improve, with respect to previous instruments, efficiency and capability to select multiplicity for fusion-evaporation reaction channels in which 1, 2 or 3 neutrons are emitted. Each NEDA detector unit has the shape of a regular hexagonal prism with a volume of about 3.23 litres and it is filled with the EJ301 liquid scintillator, that presents good neutron-γ discrimination properties. The simulations have been performed using a fusion-evaporation event generator that has been validated with a set of experimental data obtained in the 58 Ni + 56 Fe reaction measured with the Neutron Wall detector array.

The paper describes the implementation of a systolic array f o r a multiluyer perceptron on a Virtex XCV400 FPGA with a hardwure-friendl)* learning algorithm. A pipelitzed adaptation of the on-line backpropagation algorithm is shown. Parallelism is better exploited because both fonvard arid backward phases can be pelformed simultaneously.We can implement very large interconnection layers by using large Xilinx devices with embedded memories alongside the projection used in the systolic architecture. These physical and architectural features -together with the combination of FPGA reconfiguration properties with U design jlorv based on generic VHDL -create an easy, flexible, and fast method of designing a complete ANN on a single FPGA. The result offers a high degree of parallelism and fast perj%rniance.

Abstract-Current PET systems with fully digital trigger rely on early digitization of detector signals and the use of digital processors, usually FPGAs, for recognition of valid gamma events on single detectors. Timestamps are assigned and later used for coincidence analysis. In order to maintain a decent timing resolution for events detected on different acquisition boards, it is necessary that local timestamps on different FPGAs be synchronized. Sub-nanosecond accuracy is mandatory if we want this effect to be negligible on overall timing resolution. This is usually achieved by connecting all boards to a common backplane with a precise clock delivery network; however, this approach forces a rigid structure on the whole PET system and may pose scalability problems.As an alternative, we propose a backplane-less PET system architecture in which DAQ boards are connected by single fullduplex high-speed data links. Data encoding with embedded clock is used to correct frequency differences between local oscillators. Timestamp synchronization between FPGAs with clock period resolution is maintained by means of data transfers in a way similar to the IEEE 1588 standard. Finer resolution is achieved by reflection of received clocks and phase difference measurement on the transmitter. It is crucial that data transceivers have very low latency uncertainty in order to achieve the desired timestamp accuracy; we discuss the availability of off-the-shelf hardware for these implementations.

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