Electrochemical Innovation in Electronics: Is Success Sustainable? (2016)

Electrochemistry and electrochemical engineering have evolved into important disciplines enabling unprecedented progress in electronics. Because of the complexity of electronic devices the path to innovation requires a thorough understanding of materials and device characteristics, integration of materials and processes and process scale up, and control based on sound electrochemical engineering practices. Four case studies will be examined and common elements in product innovation, themes for success and shortcomings will be indentified. Because of globalization and new economic drivers of growth in developed economies, future success stories have to bring in differentiating solutions to the market place. The electrochemical fabrication of flip-chip solder technology is an extremely selective and efficient process which is extendible to larger wafer sizes, to finer dimensions and to higher C4 density. Modeling, experimental matrices, process control and equipment were developed to enable great manufacturability and scalability of the flip-chip process. 1 CIGS solar cells are one the most promising of the thin film solar cell technologies. For the electrodeposition of CIGS different processing approaches have been tried such as electrodeposition of Cu/In/Ga/Se thin films or alloy deposition of CuInGaSe 2. 2 Key challenges are the deposition of uniform nm scale thin films on meter large resistive substrates, controlling nucleation and growth and tailoring the microstructure of the thin film in order to control the composition and microstructure of the final chalcopyrite layer. 3,4 Electrodeposition of a Cu–In–Ga mixed oxide/hydroxide layer from an aqueous solution has been demonstrated. 5 Monolithically integrated power converters are enabling technologies for the fine-grain power management of future microprocessors. A high magnetic inductor Q is required for the buck converter to achieve &gt;90% power efficiency. 6 A high Q magnetic inductor poses challenging requirements for the magnetic materials: low coercivity, high permeability, high resistivity in order to minimize eddy current losses and a small damping coefficient in order to have reduce magnetic losses at high frequency. 7 We developed selection criterial for the materials based on permeability measurements and the estimation of the magnetic loss tangent. It has been a long journey of materials selection. 8 Wearables will be transformed into Thinkables offering cognitive real-time analytics of biological data at the point of sensing. Real-time analytics will require continuous streaming of large data sets. Neuromorphic platforms will play a key role in connecting on-body sensors directly with deep-learning technology creating a closed-loop interface back to the wearer. IBM’s recently introduced neuromorphic TrueNorth chip constitutes the means to transform a Wearable into a Thinkable. By linking TrueNorth technology to advances in electrochemical bio-sensing and deep-learning, we aim to create a first generation of Thinkables in the field of applied neuroscience. 9, 10 A big future need is real-time handling of large data sets. Acknowledgement The author gratefully acknowledges Luby T. Romankiw, who introduced electrochemical technology at IBM; M. Datta who had a key role in the R&amp;D of the electrochemically fabricated C4s; D. Lincot, P.-P. Grand for introducing the key challenges in solar cells and collaborating; N. Wang, E. O’Sullivan, B. Doris for collaboration on the magnetic inductor; S. Harrer and the IBM Australia team for discussions and collaboration on bioelectronics, biosensors and cognitive computing. REFERENCES M. Datta, R. V. Shenoy, C. Jahnes, P. C. Andricacos, J. Horkans, J. O. Dukovic, L. T. Romankiw, J. Roeder, H. Deligianni, H. 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Deligianni., “From Wearables to Thinkables-Deep Learning, Nano-biosensors and the Next Generation of Mobile Devices,” White Paper, ICONN 2016 , Canberra, AU. 10. E. Nurse, B. Mashford, A. Jimeno Yepes, I. Kiral-Kornek, J. Tang, P. Karoly, L. Deligianni, S. Harrer, D. Freestone, “Decoding EEG and LFP signals using deep learning: heading TrueNorth,” accepted ACM Computing Frontiers , 2016.