This paper presents a 64 x 64 pixel temporal contrast vision sensor for the 8-15 μm thermal infrared spectral range. The
device combines microbolometer detector technology with biology-inspired ('neuromorphic') focal-plane array (FPA)
processing circuitry to implement an asynchronous, 'spiking' sensor array. The sensor's individual pixels operate
autonomously and respond with low latency and high temporal resolution to changes in thermal IR radiation (temporal
contrast) by generating asynchronous, digital pulses ('spike' events). These spikes trigger the transmission of data
packets containing the active pixel's array address via an asynchronous data bus. The output data volume of such a self-timed,
event-driven sensor depends essentially on the dynamic contents of the target scene. The consequence is a near
complete suppression of image data redundancy as compared to traditional, frame-based vision sensors. We discuss the
bolometer properties and the different processing steps applied during fabrication and present a brief review of the
implemented sensor architecture. A DFT-based approach to the characterization of asynchronous, spiking sensor arrays
is introduced. We use a mechanical shutter (chopper) to generate a controllable and reproducible transient stimulus and
evaluate the pixel response in time and frequency domain. Measurement results of pixel sensitivity, bandwidth and noise
We present a cellular pulse coupled neural network with adaptive weights and its analog VLSI implementation.
The neural network operates on a scalar image feature, such as grey scale or the output of a spatial filter. It
detects segments and marks them with synchronous pulses of the corresponding neurons. The network consists
of integrate-and-fire neurons, which are coupled to their nearest neighbors via adaptive synaptic weights.
Adaptation follows either one of two empirical rules. Both rules lead to spike grouping in wave like patterns.
This synchronous activity binds groups of neurons and labels the corresponding image segments. Applications
of the network also include feature preserving noise removal, image smoothing, and detection of bright and dark
spots. The adaptation rules are insensitive for parameter deviations, mismatch and non-ideal approximation of
the implied functions. That makes an analog VLSI implementation feasible. Simulations showed no significant
differences in the synchronization properties between networks using the ideal adaptation rules and networks
resembling implementation properties such as randomly distributed parameters and roughly implemented adaptation
functions. A prototype is currently being designed and fabricated using an Infineon 130nm technology. It
comprises a 128 × 128 neuron array, analog image memory, and an address event representation pulse output.