Editorial

In recent years, we have observed spectacular advancements in the area of nano-circuits and systems at several levels, from the fabrication material and device levels to the system and application levels. New emerging materials provide us with a wealth of new devices such as (silicon) nanowires, graphene, and carbon nanotubes fabricated in various technologies. Applications of these devices are vast and include, but are not limited to, new computing and memory structures, super-capacitors, as well as nano-bio-sensors based on the molecular combination of molecular probes to electronic devices. This special issue of the Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) has the purpose to collect some selected contributions to the workshop as well as other works in this domain, all subject to peer review. In particular, this issue focuses on two specific topics: biomedical circuits and systems, and 3-D integrated circuits and systems. This choice is motivated by a synergy of the spontaneous contributions in these areas as well as by the importance of these fields. We will review these two areas at large before briefly summarizing the contributions

Thermal Management in Fine-Grained 3-D Integrated Circuits

For beyond 2-D CMOS logic, various 3-D integration approaches specially transistor based 3-D integrations such as monolithic 3-D [1], Skybridge [2], SN3D [3] holds most promise. However, such 3D architectures within small form factor increase hotspots and demand careful consideration of thermal management at all levels of integration [4] as stacked transistors are detached from the substrate (i.e., heat sink). Traditional system level approaches such as liquid cooling [5], heat spreader [6], etc. are inadequate for transistor level 3-D integration and have huge cost overhead [7]. In this paper, we investigate the thermal profile for transistor level 3-D integration approaches through finite element based modeling. Additionally, we propose generic physical level heat management features for such transistor level 3-D integration and show their application through detailed thermal modeling and simulations. These features include a thermal junction and heat conducting nano pillar. The heat junction is a specialized junction to extract heat from a selected region in 3-D; it allows heat conduction without interference with the electrical activities of the circuit. In conjunction with the junction, our proposed thermal pillars enable heat dissipation through the substrate; these pillars are analogous to TSVs/Vias, but carry only heat. Such structures are generic and is applicable to any transistor level 3-D integration approaches. We perform 3-D finite element based analysis to capture both static and transient thermal behaviors of 3-D circuits, and show the effectiveness of heat management features. Our simulation results show that without any heat extraction feature, temperature for 3-D integrated circuits increased by almost 100K-200K. However, proposed heat extraction feature is very effective in heat management, reducing temperature from heated area by up to 53%.Comment: 9 Page

An ultra-low power in-memory computing cell for binarized neural networks

Deep Neural Networks (DNN’s) are widely used in many artificial intelligence applications such as image classification and image recognition. Data movement in DNN’s results in increased power consumption. The primary reason behind the energy-expensive data movement in DNN’s is due to the conventional Von Neuman architecture in which computing unit and memory are physically separated. To address the issue of energy-expensive data movement in DNN’s in-memory computing schemes are proposed in the literature. The fundamental principle behind in-memory computing is to enable the vector computations closer to the memory. In-memory computing schemes based on CMOS technologies are of great importance nowadays due to the ease of massive production and commercialization. However, many of the proposed in-memory computing schemes suffer from power and performance degradation. Besides, some of them are capable of reducing power consumption only to a small extent and this requires sacrificing the overall signal to noise ratio (SNR). This thesis discusses an efficient In-Memory Computing (IMC) cell for Binarized Neural Networks (BNNs). Moreover, IMC cell was modelled using the simplest current computing method. In this thesis, the developed IMC cell is a practical solution to the energy-expensive data movement within the BNNs. A 4-bit Digital to Analog Converter (DAC) is designed and simulated using 130nm CMOS process. Using the 4-bit DAC the functionality of IMC scheme for BNNs is demonstrated. The optimised 4-bit DAC shows that it is a powerful IMC method for BNNs. The results presented in this thesis show this approach of IMC is capable of accurately performing dot operation between the input activations and the weights. Furthermore, 4-bit DAC provides a 4-bit weight precision, which provides an effective means to improve the overall accuracy

Electron conduction mechanism in indium oxide and its implications for amorphous transport

The electron conduction mechanism in indium oxide (In2O3) and its implications for amorphous transport have been investigated from an orbital overlap perspective. Combined density functional theory and empirical tight binding modeling reveal that the electron transport is facilitated by the neighboring metal atomic s orbital overlap “without” oxygen’s p-orbital involvement. In other words, the electron transport pathway in oxides is only due to the metal-metal medium range connection. This electron conduction mechanism is extended to amorphous In2O3 which unveils that the amorphous disorder influences the electron transport through impacting the metal-metal medium range order including metal-metal coordination number and metal-metal separation. Our results provide an insight into the current theoretical understanding of electron transport in amorphous oxide semiconductors

An SRAM Compiler for Monolithic 3D Integrated Circuit

This article presents monolithic-3-D (M3D) SRAM arrays using multiple tiers of carbon nanotube (CNT) transistors. The compiler automatically generates single-tier 2-D SRAM subarrays and multitier 3-D SRAM subarrays with different tiers for cells and peripheral logic. Moreover, the compiler can integrate multiple subarrays of different dimensions to generate larger capacity SRAM arrays. The compiler is demonstrated in a commercial-grade M3D process design kit (PDK) with two tiers of carbon nanotube transistors (CNFETs). Simulations show that the M3D CNT SRAM design can improve the properties of memory compared to the 2-D CNT SRAM design. In a 32-kB memory implementation, the M3D design can reduce footprint, latency, and energy by 33%, 10%, and 19%, respectively. The compiler is used to show the feasibility of fine-grain logic and SRAM stacking in M3D technology.M.S