Li-Ju Lin

Responses of Rabbit Retinal Ganglion Cells to Subretinal Electrical Stimulation Using a Silicon-Based Microphotodiode Array

PURPOSE With subretinal prostheses, retinal ganglion cells (RGCs) are activated by electrical stimulation of the retinal neural network. The aim of this study was to evaluate the efficacy of silicon-based solar cells in evoking RGC responses by electrically stimulating the photoreceptor side of an isolated retina. METHODS A light-bleached retina of an adult New Zealand White rabbit was placed with its photoreceptor side down onto a silicon chip that consisted of a 4 × 4 microphotodiode array (MPDA). The stimulating current was elicited by activating the solar cell with a 532-nm laser light source. Responses of the ON and OFF alpha RGCs on electrical stimulation were recorded extracellularly. Recorded RGCs were then injected with 4% N-(2-aminoethyl)-biotinamide hydrochloride to allow cell type identification. RESULTS Using a design that includes a circumvented ground electrode, the authors successfully evoked spiking responses by the ON and OFF alpha RGCs in an isolated rabbit retina using low light power to activate the MPDA (equivalent to 39 μC/cm(2)). The charge density-dependent response and the frequency-dependent pair-pulse suppression were characterized. The spike latency of the RGC responses triggered by electrical stimulation was equivalent to the latency of its light response, which supports the hypothesis that the activation is mediated by the retinal neural network. CONCLUSIONS Reliable activation of RGCs by electrical stimulation in vitro using an MPDA demonstrates the feasibility of developing solar cell-based subretinal prostheses that potentially could be developed into a power-free device able to restore vision.

Analysis and design of a CMOS angular velocity- and direction-selective rotation sensor with a retinal Processing circuit

This paper implements and analyzes a CMOS angular velocity- and direction-selective rotation sensor with a retinal processing circuit. The proposed rotation sensor has a polar structure and is selective of the angular velocity and direction (clockwise and counterclockwise) of the rotation of images. The correlation-based algorithm is adopted and each pixel in the rotation sensor is correlated with the pixel that is 45/spl deg/ apart. The angular velocity selectivity is enhanced by placing more than one pixel between two correlated pixels. The angular velocity selectivity is related to both the number and the positions of the edges in an image. Detailed analysis characterizes angular velocity selectivity for different edges. An experimental chip consisting 104 pixels, which form five concentric circles, is fabricated. The single pixel has an area of 91/spl times/84/spl mu/m/sup 2/ and a fill factor of 20%, whereas the area of the chip is 1812/spl times/1825/spl mu/m/sup 2/. The experimental results concerning the fabricated chip successfully verified the analyzed characteristics of angular velocity and direction selectivity. They showed that the detectable angular velocity and range of illumination of this rotation sensor are from 2.5/spl times/10/sup -3/ /spl pi//s to 40 /spl pi//s and from 0.91 lux to 366 lux, respectively.

The quantum-dot large-neighborhood cellular nonlinear network (QLN-CNN) in nanotechnology

The quantum-dot large-neighborhood cellular neural (nonlinear) network (QLN-CNN) is proposed and analyzed. In the proposed QLN-CNN, the quantum dots are used to realized neuron cells whereas the strength of Coulombic forces among neurons are used as weights among neurons. The proposed QLN-CNN can perform the functions of image noise removal. It has small chip area and high cell density. Moreover, the power dissipation is very low. Thus large-size QLN-CNN could be realized for nanoelectronic systems.

In the blink of a silicon eye

In this article, a new device structure called the neuron-bipolar junction transistor (/spl nu/BJT) was presented. It has been successfully applied to the design of a silicon retina and large-neighborhood cellular neural networks (CNNs). The /spl nu/BJT-based smoothing array for the silicon retina has a simple and compact structure, which is suitable for the VLSI implementation. It can be integrated with other CMOS retinal signal processing circuits to form smart sensor systems.

A new on-chip ESD protection circuit with dual parasitic SCR structures for CMOS VLSI

A novel CMOS on-chip ESD (electrostatic discharge) protection circuit which consists of dual parasitic SCR structure is proposed. Experimental results show that it can successfully provide for negative and positive ESD protection with failure thresholds greater than +or-1 kV and +or-10 kV in machine-mode (MM) and human-body-mode (HBM) testing, respectively. Moreover, low triggering voltages in both SCRs can be readily achieved without involving device or junction breakdown.<<ETX>>

A new structure of large-neighborhood cellular nonlinear network (LN-CNN)

In this paper, a novel large neighborhood cellular nonlinear network (LN-CNN) structure is proposed and analyzed. The proposed LN-CNN structure can realize both A and B templates with more than two neighborhood layers without complex direct connections between neural cell and neighboring cells. In both A and B templates, the first layer defined by 4 neighboring cells located at the 4 corners of a diamond shape whereas the second layer is defined by 8 cells. In realizing the 12 template coefficients of the template, only 8 connections are required as compared to 12 connections in the conventional CNN structure. Thus the required chip area for synaptic connection can be reduced, Using the proposed LN-CNN structure, the LN-CNN functions, such as noise removing, Muller-Layer arrowhead illusion, and connected component detection, have been successfully realized and verified in Matlab simulations. The constraints on the realized templates and template coefficients in the third or higher layers are analyzed and discussed. Based upon the above successful simulation results, the application of the proposed LN-CNN structure to the design of LN-CNN universal machine (LN-CNNUM) is quite feasible. The related research will be conducted in the future.

A new pseudo-bipolar-junction-transistor (PBJT) and its application in the design of retinal smoothing network

A new MOSFET circuit structure called the pseudo-BJT (PBJT) is proposed and analyzed in this work. This new structure mimics the function of the BJT, and it has advantages of smaller area and better process compatibility over the real BJT. The function of PBJT is verified to be similar to the real BJT via HSPICE simulation. Moreover, applications of the retinal smoothing network and the edge-extracting circuit using the PBJT structure are also demonstrated. The PBJT plays the role of the real BJT in both applications, and they are verified to be functionally correct via HSPICE simulations.

A CMOS Bio-Inspired 2-D Motion Direction Sensor Based on a Direction Computation Method Derived From the Directionally Selective Ganglion Cells in the Retina

A CMOS bio-inspired motion direction sensor structure and its associated computation method are proposed. Both method and structure with excitation-inhibition operation are derived from the directionally selective ganglion cells (DSGCs) in the retina to mimic their functions. Edge-number normalization for direction calculation and pseudo-random tessellation (PRT) structure for pixel layout arrangement are also proposed to enhance the accuracy of the computation. An experimental chip based on the proposed method and structure has been designed, fabricated, and measured. The chip comprised 32 × 32 pixels with a pixel size of 63 × 63 μm2 and a fill factor of 12.8%. The total chip size is 3.3 × 4.2 mm2 and the power consumption is 9.9 mW in the dark and 21 mW at a maximum clock rate of 10 MHz with 3.3-V power supply. The fabricated chip has been measured with different moving patterns, and a computation error of less than 11 degrees has been accomplished. This verifies the correct functions of the proposed motion direction sensor. With the capability of real-time motion detection and processing under low power dissipation, the proposed sensor is feasible for many applications.

The design of a bionic sensory chip based on the CNN model derived from the Mammalian retina

In this work, a bionic sensory chip based on the CNN model derived from the mammalian retina is proposed. A CMOS compatible sensory chip is designed based on the CNN model to mimic functions of photoreceptor and horizontal cell of the mammalian retina. The sensory chip contains a focal plane array of 32/spl times/32 similar pixels that perform functions of photoreceptor and horizontal cell. Spatial and temporal characteristics of a one-dimensional array are verified via HSPICE simulations in this paper, while a two-dimensional array of 32/spl times/32 cells is to be fabricated to investigate real-time images obtained by this bionic sensory chip.

A CMOS focal-plane rotation sensor with retinal processing circuit

In this paper, a CMOS focal-plane rotation sensor with the retinal processing circuit is proposed and implemented. The proposed rotation sensor has a polar structure and is selective to the angular velocity and direction (clockwise and counterclockwise) of the rotating images. The selected angular velocity depends on the frequency of an external clock signal, which can be precisely adjusted. In the proposed rotation sensor, there are 104 pixels, which form five concentric circles. The area of a single pixel is 91/spl times/84 /spl mu/m/sup 2/ with a fill factor of 20% whereas the chip area is 1812/spl times/1825 /spl mu/m/sup 2/ . It is found from the experimental results that the detectable angular velocity and illumination range of this rotation sensor are from 2/spl times/10/sup -2/ /spl pi//sec to 206 /spl pi//sec as well as from 0.91 lux to 366 lux under a contrast of 80%, respectively.