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We have shown that for a one-dimensional multijunction array with a broad junction length distribution, we can increase the mean threshold voltage Vth for a given maximal tunnel resistance without increasing the scatter of Vth. For two-dimensional arrays we can increase strongly the output of devices which do not behave as open circuits, without any loss on the scatter on Vth, and still increase the latter with respect to a single-island device. The experimental background which justified the calculation will be described, as our model needs the experimental distribution of the island dimensions and junction lengths. Such a result may either show a way to increase the threshold voltages for a given technology, or allow for larger and therefore more controllable dimensions, or even extend the choice of materials.

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In this paper, we aim at developing an analog spiking neural network (SNN) for reinforcing the performance of conventional cardiac resynchronization therapy (CRT) devices (also called biventricular pacemakers). Targeting an alternative analog solution in 0.13- μm CMOS technology, this paper proposes an approach to improve cardiac delay predictions in every cardiac period in order to assist the CRT device to provide real-time optimal heartbeats. The primary analog SNN architecture is proposed and its implementation is studied to fulfill the requirement of very low energy consumption. By using the Hebbian learning and reinforcement learning algorithms, the intended adaptive CRT device works with different functional modes. The simulations of both learning algorithms have been carried out, and they were shown to demonstrate the global functionalities. To improve the realism of the system, we introduce various heart behavior models (with constant/variable heart rates) that allow pathologic simulations with/without noise on the signals of the input sensors. The simulations of the global system (pacemaker models coupled with heart models) have been investigated and used to validate the analog spiking neural network implementation.

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