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Related Experiment Video

Updated: Jul 7, 2026

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Fabrication of Flexible Image Sensor Based on Lateral NIPIN Phototransistors

Published on: June 23, 2018

A bio-inspired two-layer mixed-signal flexible programmable chip for early vision.

R C Galan¹, F Jimenez-Garrido, R Dominguez-Castro

¹Inst. de Microelectron., Campus de la Univ., Sevilla, Spain.

IEEE Transactions on Neural Networks

|February 5, 2008

Summary

This study presents a bio-inspired analog programmable array processor (APAP) mimicking the vertebrate retina for efficient image processing. The prototype chip offers high computing power and low power consumption for early vision tasks.

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Area of Science:

Neuroscience and Computer Engineering
Bio-inspired computing and VLSI design

Background:

The vertebrate retina's image processing mechanisms offer a blueprint for efficient visual pathway computation.
Existing VLSI technologies present challenges for implementing complex spatio-temporal dynamics in early vision tasks.

Purpose of the Study:

To develop a bio-inspired analog programmable array processor (APAP) for early vision tasks.
To explore the feasibility of implementing retinal processing principles in standard VLSI technologies.

Main Methods:

A bio-inspired network model based on vertebrate retinal studies was developed.
An analog building block was designed and fabricated using 0.5 µm CMOS technology.
Functional tests were conducted to evaluate the prototype chip's performance.

Main Results:

The APAP successfully realized complex programmable spatio-temporal dynamics.
The prototype chip demonstrated high computing power per silicon area.
The design achieved low power consumption, competitive with existing single-chip solutions.

Conclusions:

The bio-inspired APAP model provides a feasible alternative for early vision implementation in standard technologies.
The fabricated chip showcases significant advancements in computing power and energy efficiency for visual processing.
Further research can leverage this model for advanced neuromorphic engineering applications.