CHAPTER 4 EXPERIMENTAL RESULTS
4.4 SUMMARY
Table 4.7 shows the summary of the chip for instrument measurement. There are six MOSFET transistors in single pixel circuit. The power dissipation is 14.8uW under the illumination of 736 lux.
(a)
(b)
(c)
(d)
Fig. 4.1 (a) The photograph of the basic cell in the 2-D array, (b) the photodiode in single pixel, (c) solar cells as power supply in the chip for implantation and (d) the photograph of the whole implantation chip.
(a)
Fig. 4.2 (a)The photograph of the whole implantation chip and (b) the decoder
(a)
(b)
Fig. 4.3 (a) The measurement setup chart. (b)The cross-sectional drawing of the setup for the chip.
(a)
(b)
Fig. 4.4 The power (a) and the luminance (b) of the light on the chip versus different input light power under this setup condition.
0.000
Light source
strength(W) 30 60 90 120
Corresponding
luminance (lux) 95 736 2010 3460
Table 4.1 The luminance of the light on the chip under different light source strength.
Fig. 4.5 The I-V characteristics of the solar cell in the chip for instrument measurement. The solar area is 1120umx490um.
(a)
(b)
Fig. 4.6 The open circuit voltage(a) and the short circuit current (b) of the solar cell in the chip for instrument measurement.
Open Circuit Voltage
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
30 60 90 120
Light source strength
Voltage(V)
Fig. 4.7 The flash light reaction of the solar cell in the chip for instrument measurement. The light source is yellow LED with light intensity of 212lux.
Spectral response
0 0. 05 0. 1 0. 15 0. 2 0. 25 0. 3 0. 35 0. 4 0. 45 0. 5
400 450 500 550 600 650 700
Wavelength (nm)
A/W
Fig. 4.8 The spectrum analysis of photodiode under visible light.
Fig. 4.9 The photograph of the chip for instrument measurement with opaque material shielding from light to form the light and dark boundary.
(a)
(b)
Fig. 4.10 The post-simulated waveform and the measured waveform under light intensity of 736 lux and smooth voltage of 0.9 volt (a), and under light intensity of 3460 lux and smooth voltage of 1 volt.
Table 4.2 The post-simulation condition and measurement condition for Fig. 4.10 (a).
Table 4.3 The post-simulation condition and measurement condition for Fig. 4.10 (b).
Fig. 4.11 The measured waveform of the 2-D array under light intensity of 3460 lux and smooth voltage of 1 volt.
Fig. 4.12 The response of the chip under flashlight. The flashlight intensity is 3460 lux and the smooth voltage is 1 volt.
Fig. 4.13 The measured waveform of the chip for instrument measurement. The light intensity is 3460 lux while the smooth voltage is 1 volt.
Table 4.4 The post-simulation condition and measurement condition for Fig. 4.13.
Table 4.5 The required solar cell area under different illumination but fixed photocurrent of photodiode.
Table 4.6 The required solar cell area consideration the focusing of the lens.
Fig. 4.14 Typical diffraction and slit interference pattern.
Original response Smoothing function
Fig. 4.15 The reason why the positive and negative peaks are not the same.
Table 4.7 The summary of the chip for instrument measurement.
CHAPTER 5
CONCLUSIONS AND FURTHER WORKS
5.1 MAIN RESULTS OF THIS THESIS
In this thesis, two Pseudo-BJT-based silicon retina have been designed and fabricated in a 0.35um double-poly-four-level-metal N-well CMOS technology: one is for implantation; the other is for instrument measurement. The I-V characteristics of solar cell are measured and the results proved the possibility of using solar cell as power supply of implantable artificial retina. The functions of the chip for the instrument measurement are verified by using dc power supply and solar cells. This chip operates the functions of photoreceptors, horizontal cells and bipolar cells. The power dissipation of the chip for instrument measurement is 14.8uW under light intensity of 736 lux. The solar cell area can be reduced to 399920um2 if we increase the light intensity to 5780 lux.
5.1 FURTHER WORKS
In the proposed implantable Pseudo-BJT-based silicon retina, a solution of power supply in artificial retinal prosthesis, using solar cell as power supply, has been proposed. It is compatible with standard CMOS technology. However, the solar cell area occupied large area that reduces the resolution of the artificial retina. Solar cells with higher efficiency are required.
The proposed implantable Pseudo-BJT-based silicon retina replaces the retinal cells, including photoreceptor, horizontal cell, and bipolar cell. It performs image smoothing and edge extraction functions. It breaks the function limitation of current sub-retinal prosthesis. However, there are still some retinal cells not included in this artificial retina. The stimulus signal generated by the proposed artificial retina is monophase while the optic nerve receives the biphasic stimulus. Besides, developing a silicon retina that can process color images is also essential. These will be done in the future. These will be done in the future.
REFERENCES
[1] W. Liu, E. McGucken, K. Vitchiechom and M. Clements, “Dual Unit Visual Intraocular Prosthesis,” Proc. IEEE/EMBS Annual International Conference of the IEEE ,Volume: 5 ,30 Oct.-2 Nov. 1997.
[2] W. Liu, “Retinal Implant: Bridgin Engineering and Medicine,” IEDM Digest, pp.492-495, Dec. 2002
[3] D. Marr, Vision, San Francisco, CA: W. H. Freeman, 1982.
[4] D. Marr and E. Hildreth, “Theory of Edge Detection,” Proc. Royal Soc. London B., 207, pp. 187-217,1980
[5] J. Babaud, A. P.Withkin, M. Baudin, and R. O. Duda, “Uniqueness of the Gaussian Kernel for Scale-Space Filtering,” IEEE Trans. Pattern Anal. And Mach. Intell., Vol. PAMI-8, pp. 26-33, Jan.1986.
[6] P. Perona and J. Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 12, No. 7, pp.629-639, July, 1990.
[7] A. Rosenfeld and M. Thurston, “ Edge and Curve detection for Visual Scene Analysis,” IEEE Trans. Comput., vol. C-20, pp. 562-569, May, 1971.
[8] A. Yuille and T. Poggio, “Scaling Theorems for Zero Crossings,” IEEE Trans.
Pattern Anal. Machine Intell., vol. PAMI-8, Jan., 1986.
[9] A. Witkin, “Scale-Space Filtering,” in Int., Joint Conf. Artificial Intelligence, Karlsruhe, West Germany, pp. 1019-1021, 1983.
[10] A. Hummel, “Representations Based on Zero-Crossings in Scale-Space,” in Proc. IEEE Computer Vision and Pattern Recognition Conf., pp. 204-209, June, 1986.
[11] J. F. Canny, “Finding Edges and Lines in Images,” Artificial Intelligence Lab.
Memo, No. 720, MIT, Cambridge, 1983.
[12] T. Poggio, H. Voorhees, and A. Yuille, “Regularizing Edge Detection,” Artificial Intelligence Lab. Memo, No.776, MIT, Cambridge, 1984.
[13] T. Poggio and V. Torre, “I11-posed Problems and Regularization Analysis in Early Vision,” Artificial Intelligence Lab. Memo, No.773, MIT, Cambridge, 1984.
[14] T. Poggio V. Torre and C.Koch, “Computational Vision and Regularization Theory,” Nature, vol. 317, No.6035, pp. 314-319, 1985..
[15] M. I. Sezan and R. L. Lagendijk, Motion Analysis and Image Sequence Processing, Kluwer Academic Publishers, 1993.
[16] B. K. Horn and B. G. Schunck, “Determining Optical Flow,” Artificial Intellighence, Vol. 17, pp.185-203, 1981.
[17] A. D. Bimbo, P. Nesi, and J. L. C. Sanz, “Analysis of Optical Flow Constraints,”
IEEE Trans. Image Processing, Vol. 4, pp. 460-469,1995.
[18] F. Hildreth, “ The Computation of the Velocity Field,” in Proc. Royal Soc.
London B., 1984, Vol.221, pp.189-220
[19] E. H. Adelson and J.R. Bergen, “Spatiotemporal Energy Models for the Perception of Motion,” J. Opt. Soc. Amer. A, Vol. 2, pp.284-299. Feb. 1985 [20] A. Verri, and T. Poggio, “Motion Field and Optical Flow: Qualitative Properties,”
IEEE Trans. Patt. Anal. Machine Intell., Vol. 11, pp.490-498, May 1989.
[21] A. Verri, F. Girosi and V. Torre, “Differential Techniques for Optical Flow,” J.
Opt. Soc. Amer. A, Vol. 7, pp. 912-922, May 1990.
[22] K. Fukushima, Y. Yamaguchi, M. Yasuda, and S. Nagata, “ An Electronic Model of the Retina,” IEEE Computer Society Press, 1995.
[23] C. A. Mead, Analog VLSI and Neural Systems, Addison Wesly Publishing Company, Reading, Massachusetts, 1988.
[24] C. Koch and H. Li, Vision Chips: Implementing Vision Algorithms with Analog VLSI Circuits, IEEE Computer Society Press, 1995.
[25] C. A. Mead and M.A. Mahowald, “A silicon Model of Early Visual Processing,”
Neural Networks, Vol. 1, pp.91-97, 1988.
[26] C. A. Mead, “Adaptive Retina,” Analog VLSI Implementations of Neural Systems, pp. 239-246, 1989.
[27] C. A. Mead, “Neuromorphic Electronic Systems, Proc. IEEE, vol. 78, pp.
1629-1636, 1990.
[28] T. Debruck and C.A. Mead, “Analog VLSI Phototransduction by
Continuous-Time, Adaptive, Logarithmic Photoreceptor Circuits,” Computation and Neural Systems Program, Memo, No. 30, California Institute of Technology, July 1994.
[29] K. A. Boahen and A. G. Andreou, “A Contrast Sensitive Silicon Retina with Reciprocal Synapse, “Neural Information Processing Systems, vol. 4, pp.
764-772, 1992.
[30] A. G. Androu and K. A. Boahen, “A 48,000 pixel, 59,000 transistor silicon retina in current-mode subthrshold CMOS,” Proceedings of the 37th Midwest
Symposium on Circuit and Systems, vol. 1, pp. 97-102, 1994.
[31] A. G. Androu, “Low Power Analog VLSI Systems for Sensory Information Processing,” in Microsystems Technology for Multimedia Applications, IEEE Press, edited by Sheu, Ismail, Sanchez, and Wu, Ch. 7.5, 1995.
[32] M. A. Mahowald and T. Delbruck, “Cooperative stereo Matching Using Static and Dynamic Image Features,” in Analog VKSI Implementation of Neural Systems, edited by C. mead and M. Ismail, Chapter 9, pp. 213-238, 1989.
[33] T. M. Bernard, B. Y. Zavdovique, and F. J. Devos, “A Programmable Artificial Retina,” IEEE J. Solid-State Circuits, Vol. 28, No. 7, pp. 789-798, July, 1993.
[34] J. Van der Spiegel, et al., “A Foveated Retina-Like Sensor using CCD
Technology,” in Analog VLSI Implementation of Neural Systems, C. Mead and M. Ismail Eds., 1980, pp.189-210.
[35] R. Wodnicki, G. W. Roberts, and M.D. Levine, “A Foveated Image Sensor in Standard CMOS Technology,” in Proc. Custom Integrated Circuits Conf., pp.
357-369, 1995.
[36] V. Ward and M. Syrzycki, “VLSI Implementation of Receptive Fields with Current-Mode Signal Processing,” Analog Integrated Circuits and Signal Processing, vol. 7, No. 2, pp. 167-179, 1995.
[37] C. D. Nilison, R. B. Printer, “Shunting Neural Network Photodetector Arrays in Analog CMOS,” IEEE J. Solid-State Circuits, vol. 29, No. 10, pp. 1291-1296, 1994.
[38] E. Funatsu, et al., “An Artificial Retina Chip with a 256x256 Array of n-CMOS Variable Sensitivity Photodetector Cells,” Proc. SPIE, Machine Vision
Applications, Architectures, and Systems Integration IV, vol. 2597, pp. 283-291, 1995.
[39] M. A. Massie, J.T. Woolaway, J. P. Curzan, and P. L. McCarley, “Neuromorphic Infrared Focal Spatial and Temporal Filtering, “ SPIF Proc., vol. 1961, pp.
160-174, 1993.
[40] M. Mahowald, and R. Douglas, “ A silicon Neuron,” Nature, vol. 354, pp.
515-518, 1991.
[41] C. Koch, “Resitive Networks for Computer Vision: An tutorial,” in An
Introduction to Neural and Electronic Networks, S. F. Zornetzer, J. L. Davis, and C. Lau, eds., Academic Press, New York, n. Y., 1990, pp. 293-305.
[42] S. P. Deweerth, “Analog VLSI Circuits for Stimulus Localization and Centroid,”
International Journal of Computer Vision, vol. 18, No. 9, pp. 191-202, 1992.
[43] D. L. Standley, “An Object Position and Orientation IC with Embedded Imager,”
IEEE J. Solid-State Circuits, vol. 18, No. 9, pp.1546-1548, 1994.
[44] J. G. Harris, C. Koch, and j. Luo, “A Two-Dimensional Analog VLSI Circuits for Detecting Discontinuities in Early Vision,” Science, vol. 248, pp. 1209-1211, 1990.
[45] J. G. Harris, S. C. Liu, and b. Mathur, “Discarding outlines Using a Nonlinear Resistive Network,” Proc. Int’l Joint Conf. Neural Networks, 1991, pp.239-246.
[46] S. F. Zornetzer, j. L. Dawis, and C. Lau, An Introduction to Neural and Electronic Networks, Academic Press, New York, pp. 293-305, 1990.
[47] H. Kobayashi, J. L. White, A. A. Abidi, “ An Active Resistor Network for
Gaussian Filtering of Images, “ IEEE J. Solid-State Circuits, vol. SC-26, pp.
738-748, 1991.
[48] P. C. Yu, S. J. Decker, H. S. Lee, C. G. Sodini, J. L. Wyatt, “CMOS Resistive Fuses for Image Smoothing and Segmentation,” IEEE J. Solid-State Circuits, vol. SC-27, pp. 545-553, 1992.
[49] W. Liu, K. Vichienchom, M. Clements, S.C. DeMarco, C. Hughes, E. McGucken, M. S. Humayun, E. de Juan, J. D. Weiland, and R. Greenberg, “A
Neuro-Stimulus Chip with Telemetry Unit for Retinal Prosthesis Device,” IJSSC, vol. 35, No. 10, October 2000.
[50] M. Dagtekin, W. Liu, and R. Bashirullah, “A Multi Channel Chopper Modulated Neural Recording System,” Proc. Of IEEE 23rd Annual EMBS Conf., October 2001.
[51] A. Harb, M. Sawan, and J. Zhu, “A Wireless CMOS Implantable Receiver for Neurmuscular Microstimulators,” Proc. IEEE Communications, Power, and Computing Conf., vol. 2, May 1995.
[52] R. Bashirullah, W. Liu, Y. Ji, Alper Kendir, M. Sivaprakasam, G. Wang, and B.
Paudi, “A Smart Bi-directional Telemetry Unit for Rtinal Prosthetic Device,”
ISCAS, vol. 5, May 2003.
[53] M. Clements, K. Vichienchom, W. Liu, C. Hughes, E. McGucken, C. Demarco, J.
Mueller, “An Implantable Power and Data Receiver and Neuro-Stimulus Chip for A Retinal Prosthesis System,” ISCAS, vol. 1, May 1999.
[54] A. Y. Chow, T. Pradue, J. I. Perlman, and etc, “Subretinal Implantation of Semiconductor-Based Photodiodes: Durability of novel implant designs,”
Journal of rehabilitation Research and Development, Vol.39, No.3, pp.313-322, 2002.
[55] http://www.optobionics.com/theeye.htm
[56] F. S. Werblin, and J. E. Dowling, “ Organization of the Retina of the Mudpuppy, Necturus Maculosus, II, Intracellular Recording,” J. Neurophysiol., 32:339-355, 1969.
[57] G. L. Walls, The Vertebrate Eye and Its Adaptive Radiation, New York: Hafner, 1942.
[58] H. B. Barlow and W. R. Levick, “ The Mechanism of Directionally Selective Units in the Rabbit’s Retina,” J. Physiol., 178:477-504, 1965.
[59] C. R. Michael, “Receptive Fields of Single Optic Nerve Fiber in a Mammal with an All-Cone Retina, II, Directionally Selective Units,” J. NeuroPhysiol.,
31:257-267, 1968.
[60] J. E. Dowling, “Organization of Vertebrate Retinas,” invest. Ophthalmmol.,
9:655-680, 1970.
[61] M. Ariel, and N. W. Daw, “ Pharmacological Analysis of directionally Seletive Rabbit Retinal Ganglion Cells,” J. Physiol., 324:161-185, 1982.
[62] J. E. Dowling, “Information Processing by Local Circuits: the Vertebrate Retina as a Model System,” In The Neurosciences: Fourth Study Program, Ed. F. O.
Schmitt and F. G. Worden, Cambridge, Mass.: MIT Press, pp. 213-216, 1979.
[63] M. S. Humayun, “Is Surface Electrical Stimulation of the Retina a Feasible Approach towards the Development of a Visual Prosthesis?” PhD Dissertation, University of North Carolina at Chapel Hill, 1994.
[64] R. F. Pierret, Semiconductor Device Fundamentals, Addison Wesley, 1996.
[65] C. Y. Wu, H. C. Huang, L. J. Lin, and K. H. Huang, “A New
Pseudo-Bipolar-Junction-Transistor (PBJT) and its application in the Design of Retinal Smoothing Network,” in Proc. IEEE International Symposium on Circuits and Systems, vol. 4, pp. 125-128, July, 2002.
[66] J.H. Huang, Z.H. Liu, M.C. Jeng, K. Hui, M. Chan, P. K. ko, and C. Hu., BSIM Version 3.2.2 MOSFET Model User’s Manual, 1999
[67] Bart Van Zeghbroeck, Principles of Semiconductor Devices, 2004.