Microelectronics Engineering

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http://etd.aau.edu.et/handle/12345678/194

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Browsing Microelectronics Engineering by Author "Getachew, Alemu (PhD)"

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    Integration of Electronics and Photonics on Monolithic Silicon to Improve the Performance of Conventional Electronic Devices
    (AAU, 2016-12) Hagos, Gebreyesus; Getachew, Alemu (PhD)
    Conventional electronics have shown dramatic improvement in dimensions and performances over the last four decades and plays a great role in computing, storing, transmitting and retrieving data in all electronic devices. However, chip-to-chip and on-chip interconnect which have affected by a parasitic load became the real performance bottleneck due to its extremely reduced cross section dimension along with moore‘s law. Now, there are metal (copper) interconnections and dielectric materials in IC fabrications that faces great challenges for the future in the electronics, imposing problems of interconnect that the performance and functionality of conventional electronic devices are leading their physical limit in speed, bandwidth, power consumption (heating) and electromagnetic interference. In contrary, photonic devices have advantages like large bandwidth, lower power consumption (low heating) and immune to electromagnetic interference. So an integrated electro-photonic interconnect have seen an alternate solution for the future technology nodes due to their special physical characteristics. Therefore in order to overcome these electronic limitations ha ve been faced, an integrated electronics and photonics on monolithic silicon substrate has been proposed as a potential solution by merging the advantage of both technologies, electronics for data processing and storing while photonics for both on-chip and off-chip interconnection to obtain a future supper computing device. To study the integration of electronics and photonics on monolithic silicon substrate to improving the performance of conventional electronic devices, two experimental set up were prepared on laboratory at device level. The first experimental set up was integrated electronic and photonic interconnect. Its components were connected with waveguide or glass rod and represented by electrophotonic interconnect. The second experimental set up was conventional electronic circuit. Its components were connected by copper wire (Cu). Both experiments had similar three different lengths (30, 10, and 5) cm of Cu wire and waveguide at  of 1mm and 1cm respectively for both voltage and current output measurements from the system. And both experiments had again similar three different  (13, 9, and 6) mm of Cu wires and (1, 0.5, and 0.2) mm of waveguides at equal length of 25cm for delay and powe r measurements in the system at different clock frequencies (50, 75 and diameters 100) kHz. Comparisons were made and based on the result found electrophotonic interconnect performed better than that of the Cu interconnect in terms of delay and power. Therefore, delay and power dissipation on Cu interconnect was higher than electrophotonic interconnect by 62.8% and 60% respectively.
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    Investigation of Soft Neural Network Algorithm Implement to Analog Electronics Devices
    (Addis Ababa University, 2018-12-31) Eyob, Gedlie; Getachew, Alemu (PhD)
    The implementation of neural systems is presented in this paper. The thesis focuses on implementations where the algorithms and their physical support are tightly coupled. This thesis describes a neural network intelligent, application, soft-algorithm to implement to hardware electronics device. With the emerging of Integrated Circuit, any design with large number of electronic components can be squeezed into a tiny chip area with minimum power requirements, which leads to integration of innumerable applications so as to design any electronic consumer product initiated in the era of digital convergence. One has many choices for selecting either of these reconfigurable techniques based on Speed, Gate Density, Development, Prototyping, simulation time and cost. This thesis describes the implementation in hardware of an Artificial Neural Network with an Electronic circuit made up of Op-amps. The implementation of a Neural Network in hardware can be desired to benefit from its distributed processing capacity or to avoid using a personal computer attached to each implementation. The hardware implementation is based in a Feed Forward Neural Network, with a hyperbolic tangent as activation function, with floating point notation of single precision. The device used was an electronic circuit made with Op-amps The Proteus Software version 8.0 was used to validate the implementation results of the hardware circuit. The results show that the implementation does not introduce a noticeable loss of precision but is slower than the software implementation running in a PC.