Can Akgun

1. A 10-bit, 771 nW Time-Mode ADC with a 2-Step TDC for Bio-Signal Acquisition
   E. Kandilakis; W.A. Serdijn; O.C. Akgun;
   In proc. 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS),
   IEEE, Oct. 6-9 2021. DOI: 10.1109/BioCAS49922.2021.9644997
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2. A Chip Integrity Monitor for Evaluating Moisture/Ion Ingress in mm-Sized Single-Chip Implants
   Omer Can Akgun; Kambiz Nanbakhsh; Vasiliki Giagka; Wouter Serdijn;
   IEEE Transactions on Biomedical Circuits and Systems,
   7 July 2020. DOI: 10.1109/TBCAS.2020.3007484
   
   Keywords: ...

   —Chip integrity, flexible implants, encapsulation, interlayer dielectric (ILD), silicon dioxide, resistance, time-mode, monitoring, reliability.
   
   document

3. An energy efficient time-mode digit classification neural network implementation
   Omer Can Akgun; J. Mei;
   Philosophical Transactions of the Royal Society A - Mathematical, Physical and Engineering Sciences,
   23 December 2019. DOI: 10.1098/rsta.2019.0163
   
   Abstract: ...

   This paper presents the design of an ultra-low energy neural network that uses time-mode signal processing). Handwritten digit classification using a single-layer artificial neural network (ANN) with a Softmin-based activation function is described as an implementation example. To realize time-mode operation, the presented design makes use of monostable multivibrator-based multiplying analogue-to-time converters, fixed-width pulse generators and basic digital gates. The time-mode digit classification ANN was designed in a standard CMOS 0.18 μm IC process and operates from a supply voltage of 0.6 V. The system operates on the MNIST database of handwritten digits with quantized neuron weights and has a classification accuracy of 88%, which is typical for single-layer ANNs, while dissipating 65.74 pJ per classification with a speed of 2.37 k classifications per second. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
   
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4. ENERGY EFFICIENT SAMPLING AND CONVERSION OF BIO-SIGNALS USING TIME-MODE CIRCUITS
   Omer Can Akgun; Wouter A. Serdijn;
   In Book of Abstracts, 7th Dutch Biomedical Engineering Conf. (BME) 2019,
   Jan. 24-25 2019.
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5. An Energy-Efficient Multi-Sensor Compressed Sensing System Employing Time-Mode Signal Processing Techniques
   Omer Can Akgun; Mauro Mangia; Fabio Pareschi; Riccardo Rovatti; Gianluca Setti; Wouter A. Serdijn;
   In proc. 2019 IEEE International Symposium on Circuits and Systems (ISCAS'2019),
   IEEE, 26-29 May 2019. DOI: 10.1109/ISCAS.2019.8702667
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6. A Chip Integrity Monitor for Evaluating Long-term Encapsulation Performance Within Active Flexible Implants
   Omer Can Akgun; Kambiz Nanbakhsh; Vasiliki Giagka; Wouter A. Serdijn;
   In Proc. IEEE Biomedical Circuits and Systems Conference (BioCAS 2019),
   IEEE, October 17-19 2019.
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7. 11nW Signal Acquisition Platform for Remote Biosensing
   Alberto Gancedo; Omer Can Akgun; Wouter A. Serdijn;
   In Proc. IEEE Biomedical Circuits and Systems Conference (BioCAS 2019),
   IEEE, October 17-19 2019.
   document

8. An Asynchronous Pipelined Time-to-Digital Converter Using Time-Domain Subtraction
   Omer Can Akgun;
   In proc. International Symposium on Circuits and Systems,
   Florence, Italy, May, 27-30 2018.
   document

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