Low-temperature fabrication of spiking soma circuits using nanocrystalline-silicon TFTs.

Spiking neuron circuits consisting of ambipolar nanocrystalline-silicon (nc-Si) thin-film transistors (TFTs) have been fabricated using low temperature processing conditions (maximum of 250 °C) that allow the use of flexible substrates. These circuits display behaviors commonly observed in biological neurons such as millisecond spike duration, nonlinear frequency-current relationship, and spike frequency adaptation. The maximum drive capacity of a simple soma circuit was estimated to be approximately 9200 synapses. The effect of bias stress-induced threshold voltage degradation of component nc-Si TFTs on the spike frequency of soma circuits is explored. The measured power consumption of the circuit when spiking at 100 Hz was approximately 12 nW. Finally, the power consumption of the soma circuits at different spiking conditions and its implications on a large-scale system are discussed. The fabricated circuits can be employed as part of a compact multilayer learning network.

Neural learning circuits utilizing nano-crystalline silicon transistors and memristors.

Properties of neural circuits are demonstrated via SPICE simulations and their applications are discussed. The neuron and synapse subcircuits include ambipolar nano-crystalline silicon transistor and memristor device models based on measured data. Neuron circuit characteristics and the Hebbian synaptic learning rule are shown to be similar to biology. Changes in the average firing rate learning rule depending on various circuit parameters are also presented. The subcircuits are then connected into larger neural networks that demonstrate fundamental properties including associative learning and pulse coincidence detection. Learned extraction of a fundamental frequency component from noisy inputs is demonstrated. It is then shown that if the fundamental sinusoid of one neuron input is out of phase with the rest, its synaptic connection changes differently than the others. Such behavior indicates that the system can learn to detect which signals are important in the general population, and that there is a spike-timing-dependent component of the learning mechanism. Finally, future circuit design and considerations are discussed, including requirements for the memristive device.

One-step selective chemistry for silicon-on-insulator sensor geometries.

A one-step functionalization process has been developed for oxide-free channels of field effect transistor structures, enabling a self-selective grafting of receptor molecules on the device active area, while protecting the nonactive part from nonspecific attachment of target molecules. Characterization of the self-organized chemical process is performed on both Si(100) and SiO(2) surfaces by infrared and X-ray photoelectron spectroscopy, atomic force microscopy, and electrical measurements. This selective functionalization leads to structures with better chemical stability, reproducibility, and reliability than current SiO(2)-based devices using silane molecules.