In this paper, we propose a paradigm shift in representing and optimizing logic by using only majority (MAJ) and inversion (INV) functions as basic operations. We represent logic functions by majority-inverter graph (MIG): a directed acyclic graph consisting of three-input majority nodes and regular/complemented edges. We optimize MIGs via a new Boolean algebra, based exclusively on majority and inversion operations, that we formally axiomatize in this paper. As a complement to MIG algebraic optimization, we develop powerful Boolean methods exploiting global properties of MIGs, such as bit-error masking. MIG algebraic and Boolean methods together attain very high optimization quality. Considering the set of IWLS'05 benchmarks, our MIG optimizer (MIGhty) enables a 7% depth reduction in LUT-6 circuits mapped by ABC while also reducing size and power activity, with respect to similar and-inverter graph (AIG) optimization. Focusing on arithmetic intensive benchmarks instead, MIGhty enables a 16% depth reduction in LUT-6 circuits mapped by ABC, again with respect to similar AIG optimization. Employed as front-end to a delay-critical 22-nm application-specified integrated circuit flow (logic synthesis + physical design) MIGhty reduces the average delay/area/power by 13%/4%/3%, respectively, over 31 academic and industrial benchmarks. We also demonstrate delay/area/power improvements by 10%/10%/5% for a commercial FPGA flow.

This paper reviews some popular techniques to harvest energy for implantable biosensors. For each technique, the advantages and drawbacks are discussed. Emphasis is placed on the inductive links that are able to deliver power wirelessly through the biological tissues and enable bidirectional data communication with the implanted sensors. Finally, high-frequency inductive links are described, focusing also on the power absorbed by the tissues.

Background: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. Results: We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. Conclusion: The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.

3D sequential integration enables the full use of the third dimension thanks to its high alignment performance. In this paper, we address the major challenges of 3D sequential integration: in particular, the control of molecular bonding allows us to obtain pristine quality top active layer. With the help of Solid Phase Epitaxy, we can match the performance of top FET, processed at low temperature (600°C), with the bottom FET devices. Finally, the development of a stable salicide enables to retain bottom performance after top FET processing. Overcoming these major technological issues offers a wide range of applications.

As the current MOSFET scaling trend is facing strong limitations, technologies exploiting novel degrees of freedom at physical and architecture level are promising candidates to enable the continuation of Moore's predictions. In this paper, we report on the fabrication of novel ambipolar Silicon nanowire (SiNW) Schottky-barrier (SB) FET transistors featuring two independent gate-all-around electrodes and vertically stacked SiNW channels. A top-down approach was employed for the nanowire fabrication, using an e-beam lithography defined design pattern. In these transistors, one gate electrode enables the dynamic configuration of the device polarity (n - or p-type) by electrostatic doping of the channel in proximity of the source and drain SBs. The other gate electrode, acting on the center region of the channel switches ON or OFF the device. Measurement results on silicon show I _on /I _off >10 ⁶ and subthreshold slopes approaching the thermal limit, ≈ 64 mV/dec (70 mV/dec) for p(n)-type operation in the same physical device. Finally, we show that the XOR logic operation is embedded in the device characteristic, and we demonstrate for the first time a fully functional two-transistor XOR gate.