Review Article  Open Access
FinFETs: From Devices to Architectures
Abstract
Since Moore’s law driven scaling of planar MOSFETs faces formidable challenges in the nanometer regime, FinFETs and Trigate FETs have emerged as their successors. Owing to the presence of multiple (two/three) gates, FinFETs/Trigate FETs are able to tackle shortchannel effects (SCEs) better than conventional planar MOSFETs at deeply scaled technology nodes and thus enable continued transistor scaling. In this paper, we review research on FinFETs from the bottommost device level to the topmost architecture level. We survey different types of FinFETs, various possible FinFET asymmetries and their impact, and novel logiclevel and architecturelevel tradeoffs offered by FinFETs. We also review analysis and optimization tools that are available for characterizing FinFET devices, circuits, and architectures.
1. Introduction
Relentless scaling of planar MOSFETs over the past four decades has delivered everincreasing transistor density and performance to integrated circuits (ICs). However, continuing this trend in the nanometer regime is very challenging due to the drastic increase in the subthreshold leakage current () [1–3]. Due to the very narrow channel lengths in deeply scaled MOSFETs, the drain potential begins to influence the electrostatics of the channel and, consequently, the gate loses adequate control over the channel. As a result, the gate is unable to shut off the channel completely in the offmode of operation, which leads to an increased between the drain and the source. The use of thinner gate oxides and highk dielectric materials helps alleviate this problem by increasing the gatechannel capacitance. However, thinning of gate oxides is fundamentally limited by the deterioration in gate leakage and gateinduced drain leakage (GIDL) [4–6]. Multiplegate fieldeffect transistors (MGFETs), which are an alternative to planar MOSFETs, demonstrate better screening of the drain potential from the channel due to the proximity of the additional gate(s) to the channel (i.e., higher gatechannel capacitance) [7–12]. This makes MGFETs superior to planar MOSFETs in shortchannel performance metrics, such as subthreshold slope (), draininduced barrier lowering (DIBL), and threshold voltage () rolloff. Improvement in these metrics implies less degradation in the transistor’s with continued scaling, which in turn implies less degradation in .
So far, we have referred to planar MOSFETs built on bulkSi wafers (or bulk MOSFETs) as planar MOSFETs. Fullydepleted silicononinsulator (FDSOI) MOSFETs (planar MOSFETs built atop SOI wafers) avoid the extra leakage paths from the drain to source by getting rid of the extra substrate beneath the channel [13, 14]. Their performance metrics are comparable with those of doublegate FETs (DGFETs), which are MGFETs with two gates. Both offer reduced junction capacitance, higher ratio, better , and improved robustness against random dopant fluctuation (RDF). However, DGFETs have a more relaxed constraint on channel thickness, which makes DGFETs more scalable than FDSOI MOSFETs in the long run [15, 16]. Also, DGFET structures can be built on bulkSi wafers, as well, which makes DGFETs more attractive to foundries that do not want to switch to an SOI process [17, 18].
Among all MGFETs, FinFETs (a type of DGFET) and Trigate FETs (another popular MGFET with three gates) have emerged as the most desirable alternatives to MOSFETs due to their simple structures and ease of fabrication [19–27]. Two or three gates wrapped around a vertical channel enable easy alignment of gates and compatibility with the standard CMOS fabrication process. In Trigate FETs, an additional selective etching step of the hard mask is involved in order to create the third gate on top of the channel. Although this third gate adds to process complexity, it also leads to some advantages like reduced fringe capacitances and additional transistor width [28–30].
FinFET/Trigate devices have been explored thoroughly in the past decade. A large number of research articles have been published that demonstrate the improved shortchannel behavior of these devices over conventional bulk MOSFETs [19–22, 31–33]. Many researchers have presented novel circuit design styles that exploit different kinds of FinFETs [34–48]. Researchers have also explored various symmetric and asymmetric FinFET styles and used them in hybrid FinFET logic gates and memories [49–66]. Newer architectures for caches, networksonchip (NoCs), and processors based on such logic gates and memories have also been explored [67–74]. In spite of these advancements in FinFET research, articles that provide a global view of FinFETs from the device level to the topmost architecture level are scarce. Mishra et al. provided such a view at the circuit level [75]. However, FinFETs are not covered at other levels of the design hierarchy. Also, at the circuit level, much progress has been made since the publication of that book chapter. Our article is aimed at a wide range of readers: device engineers, circuit designers, and hardware architects. Our goal is to provide a global view of FinFET concepts spanning the entire IC design hierarchy.
The paper is organized as follows. In Section 2, we review the different types of FinFETs and possible asymmetries that can be designed into their structures. We also discuss the sources of process variations in FinFETs and their impact on FinFET performance. We discuss FinFET process simulation, device simulation, and compact models in Section 3. We describe novel FinFET inverter (INV) and NAND gates, flipflops, latches, static randomaccess memory (SRAM), and dynamic randomaccess memory (DRAM) cells in Section 4. In Section 5, we discuss circuitlevel analysis and optimization methodologies and a novel interconnect scheme that leverages FinFETs. We then present a survey of processvoltagetemperature (PVT) variationaware architecturelevel simulation tools in Section 6 and conclude in Section 7.
2. FinFETs
In 1989, Hisamato et al. fabricated a doublegate SOI structure which they called a fullydepleted lean channel transistor (DELTA) [76]. This was the first reported fabrication of a FinFETlike structure. FinFETs have attracted increasing attention over the past decade because of the degrading shortchannel behavior of planar MOSFETs [19–24]. Figure 1 demonstrates the superior shortchannel performance of FinFETs over planar MOSFETs with the same channel length. Figure 2 shows a conventional planar MOSFET and a FinFET. While the planar MOSFET channel is horizontal, the FinFET channel (also known as the fin) is vertical. Hence, the height of the channel () determines the width () of the FinFET. This leads to a special property of FinFETs known as width quantization. This property says that the FinFET width must be a multiple of , that is, widths can be increased by using multiple fins. Thus, arbitrary FinFET widths are not possible. Although smaller fin heights offer more flexibility, they lead to multiple fins, which in turn leads to more silicon area. On the other hand, taller fins lead to less silicon footprint, but may also result in structural instability. Typically, the fin height is determined by the process engineers and is kept below four times the fin thickness [77, 78].
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Although FinFETs implemented on SOI wafers are very popular, FinFETs have also been implemented on conventional bulk wafers extensively [79–81]. Figure 3 shows FinFETs implemented on bulk and SOI wafers. Unlike bulk FinFETs, where all fins share a common Si substrate (also known as the bulk), fins in SOI FinFETs are physically isolated. Some companies prefer the bulk technology because it is easier to migrate to bulk FinFETs from conventional bulk MOSFETs. However, FinFETs on both types of wafers are quite comparable in terms of cost, performance, and yield, and it is premature to pick a winner. From this point on, our discussion will be limited to SOI FinFETs unless otherwise mentioned.
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Trigate FETs, referred to interchangeably as FinFETs, in this paper so far, are a variant of FinFETs, with a third gate on top of the fin. Intel introduced Trigate FETs at the 22 nm node in the IvyBridge processor in 2012 [28, 82]. Figure 4 shows a Trigate FET along with a FinFET. The thickness of the dielectric on top of the fin is reduced in Trigate FETs in order to create the third gate. Due to the presence of the third gate, the thickness of the fin also adds to the channel width. Hence, Trigate FETs enjoy a slight width advantage over FinFETs. Trigate FETs also have less gatesource capacitance compared to FinFETs due to additional current conduction at the top surface, but this advantage is diminished by increased parasitic resistance [29].
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Yang and Fossum compared Trigate FETs and FinFETs and argued that FinFETs are superior to Trigate FETs in the long run [83]. They showed that although undoped Trigate FETs may enjoy more relaxed body thickness, they are not competitive with FinFETs in SCE metrics. When trying to achieve comparable SCE metrics, Trigate FETs lose the scaling advantage and suffer from significant layout area disadvantage. However, like the bulk versus SOI debate, it is also premature to declare a clear winner between FinFETs and Trigate FETs. From this point onwards, we will consider FinFETs only unless stated otherwise.
FinFETs can be fabricated with their channel along different directions in a single die. Fabrication of planar MOSFET channels along any crystal plane other than is difficult due to process variations and interface traps [36, 84]. However, FinFETs can be fabricated along the plane as well. This results in enhanced hole mobility. oriented FinFETs can be fabricated by simply rotating the transistor layout by in the plane of a wafer [85]. Thus, nFinFETs implemented along and pFinFETs along lead to faster logic gates since this gives designers an opportunity to combat the inherent mobility difference between electrons and holes. However, this multiorientation scheme has an obvious drawback of increased silicon area [85]. In the following sections, we discuss FinFET classifications and process variations in detail.
2.1. FinFET Classification
There are two main types of FinFETs: shortedgate (SG) and independentgate (IG). SG FinFETs are also known as threeterminal (3T) FinFETs and IG FinFETs as fourterminal (4T) FinFETs. In SG FinFETs, both the front and back gates are physically shorted, whereas in IG FinFETs, the gates are physically isolated (Figure 5). Thus, in SG FinFETs, both gates are jointly used to control the electrostatics of the channel. Hence, SG FinFETs show higher oncurrent () and also higher offcurrent ( or the subthreshold current) compared to those of IG FinFETs. IG FinFETs offer the flexibility of applying different signals or voltages to their two gates. This enables the use of the backgate bias to modulate the of the front gate linearly. However, IG FinFETs incur a high area penalty due to the need for placing two separate gate contacts.
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SG FinFETs can be further categorized based on asymmetries in their device parameters. Normally, the workfunctions () of both the front and back gates of a FinFET are the same. However, the workfunctions can also be made different. This leads to an asymmetric gateworkfunction SG FinFET or ASG FinFET (Figure 6) [86, 87]. ASG FinFETs can be fabricated with selective doping of the two gatestacks. They have very promising shortchannel characteristics and have two orders of magnitude lower compared to that of an SG FinFET, with only somewhat lower than that of an SG FinFET [49]. Figures 7 and 8 show comparisons of the drain current versus frontgate voltage curves for SG, IG, and ASG nFinFETs and pFinFETs, respectively, demonstrating the advantages of ASG FinFETs.
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Apart from gateworkfunction asymmetry, other asymmetries have also been explored in FinFETs. Goel et al. [57] show that asymmetric drainspacerextended (ADSE) FinFETs (Figure 9) can lead to improved shortchannel characteristics because of an indirect increase in channel length. However, this improvement comes at the cost of an increased layout area. This asymmetry also destroys the conventional interchangeable sourcedrain concept in CMOS. An asymmetry is created in the draintosource current and sourcetodrain current because of the extra underlap. This asymmetry affects FinFET pass transistor performance. Asymmetric drainsource doped (AD) FinFETs (Figure 10), with an order of magnitude difference in the drain and source doping concentrations, have been exploited in [58]. This also destroys the conventional symmetry in and , which again leads to asymmetric FinFET pass transistor performance. SCEs are improved in AD FinFETs because of lower electric fields in the lowerdoped drain. FinFETs with asymmetric oxide thickness (ATox) (Figure 11) have also been proposed [88, 89]. Such FinFETs have good subthreshold slopes. Use of IG FinFET (or 4T FinFET) in this context also enables variable ’s. This asymmetry can be achieved using a ionbombardmentenhanced etching process. Finally, asymmetric finheight FinFETs have also been explored [61, 90]. Since the channel width of a FinFET is proportional to its fin height, pFinFETs with taller fins can compensate for the inherent mobility mismatch between electrons and holes.
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Figure 12 shows a twodimensional (2D) crosssection of a threedimensional (3D) FinFET, illustrating various device parameters of interest. Typical values for these parameters are given in Table 1. , , , , , , , , , , , , , , , , and refer to the physical front and backgate lengths, front and backgate effective oxide thicknesses, fin thickness, fin height, front and backgate thicknesses, front and backgate spacer thicknesses, gatedrain/source underlap, body doping, source/drain doping, front and backgate workfunctions, fin pitch, and gate pitch, respectively.

2.2. Process Variations
Reduced feature size and limited photolithographic resolution cause statistical fluctuations in nanoscale device parameters. These fluctuations cause variations in electrical device parameters, such as , , , and so forth, known as process variations. These variations can be interdie or intradie, correlated or uncorrelated, depending on the fabrication process. They lead to mismatched device strengths and degrade the yield of the entire die. This is why continued scaling of planar MOSFETs has become so difficult.
In planar MOSFETs, a sufficient number of dopants must be inserted into the channel in order to tackle SCEs. However, this means that RDF may lead to a significant variation in . For example, at deeply scaled nodes, the 3 variation in caused by discrete impurity fluctuation can be greater than 100% [91]. Since FinFETs enable better SCE performance due to the presence of the second gate, they do not need a high channel doping to ensure a high . Hence, designers can keep the thin channel (fin) at nearly intrinsic levels (). This reduces the statistical impact of RDF on . The desired is obtained by engineering the workfunction of the gate material instead. Low channel doping also ensures better mobility of the carriers inside the channel. Thus, FinFETs emerge superior to planar MOSFETs by overcoming a major source of process variation.
FinFETs do suffer from other process variations. Due to their small dimensions and lithographic limitations, FinFETs are subjected to several important physical fluctuations, such as variations in gate length (, ), finthickness (), gateoxide thickness (, ), and gate underlap () [91–97]. For example, gate oxide is on the etched sidewall of the fin, and may suffer from nonuniformity. The degree of nonuniformity depends on the lineedge roughness (LER) of the fin. LER also causes variations in fin thickness. Figure 13 shows the impact of parametric variations on the subthreshold current () of an nFinFET. Xiong and Bokor have studied the sensitivity of electrical parameters to various physical variations in devices designed with a nearly intrinsic channel [91].
Choi et al. have studied temperature variations in FinFET circuits under abovementioned physical parameters variations [98]. They showed that even under moderate process variations (3() = 10%) in gate length (, ) and body thickness (), thermal runaway is possible in more than 15% of ICs when primary input switching activity is 0.4. The effect of temperature variation is more severe in SOI FinFETs because the oxide layer under the fin has poor thermal conductivity. Hence, heat generated in the fin cannot dissipate easily in SOI FinFETs. Bhoj and Jha have evaluated SG, IG, and ASG FinFETs under temperature variation and found that even though degrades for all three FinFETs at a higher temperature, ASG FinFETs still remain the best and retain a 100 advantage over SG FinFETs, as shown in Figure 14 [49]. They also showed the distribution of under process variations for the three FinFETs (Figure 15).
3. FinFET Device Characterization
In this section, we discuss various ways of characterizing FinFET devices through simulation. Process simulation followed by device simulation constitutes a technology computeraided design (TCAD) characterization flow of nanoscale devices, such as FinFETs. Compact models, on the other hand, have been another very popular way of characterizing CMOS devices for decades.
3.1. Process Simulation
Real devices undergo several processing steps. The functionality and performance of the fabricated devices depend on how optimized the process flow is. TCAD process simulation is, therefore, an important step in FinFET device optimization. Process simulation is followed by device simulation. These two simulation steps form an optimization loop in which small changes in the process flow (e.g., time, temperature, doses, etc.) can lead to desirable electrical characteristics of the device. Thus, process simulation helps device engineers explore the parameter space of the process, obviating the need for actual device fabrication. Although 3D process simulation is computationally very expensive, it not only gives good insights into device physics but also provides a costeffective prefabrication process optimization flow.
The Sentaurus process and device simulator from Synopsys is a widely used tool for process simulation [99]. Its 3D process simulation framework is compatible with the mainstream 2D TCAD framework TSUPREM4/MEDICI (also from Synopsys). The 2D framework has been used by designers over the past decade and has been wellcalibrated with advanced CMOS libraries. Nawaz et al. have implemented a complete FinFET process flow as a commerciallyavailable process and device simulation environment [100]. As in real devices, all important geometrical features, such as corner roundings and 3D facets, have been implemented in their setup.
Process simulations of large layouts that consist of multiple devices incur extremely high computational costs. A novel layout/process/deviceindependent TCAD methodology was proposed in [54] in order to overcome the process simulation barrier for accurate 3D TCAD structure synthesis. In it, Bhoj et al. adopt an automated structure synthesis approach that obviates the need for repetitive 3D process simulations for different layouts. In this approach, processsimulated unit devices are placed at the device locations in the layout, eliminating the need for process simulation of the entire layout, thereby reducing computational costs significantly. This structure synthesis approach, followed by transport analysis based capacitance extraction methodology, has been shown to capture accurate parasitic capacitances in FinFET SRAMs and ring oscillators in a practical timeframe [54, 55, 63, 66]. Accurate extraction of parasitic capacitances has led to a comprehensive evaluation of transient metrics of various FinFET SRAM bitcells [55].
3.2. Device Simulation
After process simulation generates a meshed device structure, device simulation is performed on the structure by invoking appropriate transport models. The conventional driftdiffusion transport model is not adequate for capturing SCEs in nanometer MOSFETs and FinFETs. The hydrodynamic model, with quantum corrections (such as density gradient models), has been popular among researchers for FinFET device simulation [101]. Other more accurate models, such as Green’s function based solution to Boltzmann’s transport equation, impose a drastic computational burden [101]. In order to simulate circuits with multiple devices, Sentaurus device (Synopsys) allows mixedmode device simulation. Here, individual FinFET devices are connected externally using wires or other circuit elements to form a netlist and coupled transport equations are solved on the entire netlist. This feature enables device engineers to see how the device behaves when used in a circuit.
3.3. Compact Models
Physics based compact models of FinFETs have been a very useful tool for designers. Berkeley shortchannel IGFET model (BSIM) and University of Florida doublegate model (UFDG) for SOI multigate MOSFETs and FinFETs were built using TCAD and calibrated using fabricated hardware [102–105]. These models are compatible with commercial circuit simulators, such as simulation program with integrated circuit emphasis (SPICE). Hence, large netlists can be simulated with these models as long as the solution space is within their range. However, device simulation precedes derivation of compact models and is more accurate. Thus, all results presented in this article are based on mixedmode device simulations.
4. FinFET Standard Cells
After the characterization of individual n/pFinFET devices, we move one level up to characterization of FinFET logic gates, latches, flipflops, and memory cells, which are the building blocks of any digital integrated circuit [49–51]. IG and ASG FinFETs offer new leakagedelay tradeoffs in FinFET logic gates that can be exploited in lowpower or highperformance applications. The schematic diagrams of SG and IG FinFETs are shown in Figure 16. Schematic diagrams of ASG FinFETs are shown in Figure 17. Bhoj and Jha have performed an indepth analysis and comparison of SG, IG, and ASG FinFET based INV and NAND2 (twoinput NAND) gates [49]. These two gates are the most essential building blocks of any logic library because any logic network can be built with just these two gates.
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4.1. SG/IG INV
There are four possible configurations of an INV based on how SG and IG FinFETs are combined to implement them. They are called SG, lowpower (LP), IGn, and IGp INV. Their schematic diagrams are shown in Figure 18. As suggested by its name, an SG INV has SG n/pFinFETs. It has a highly compact layout. The other three configurations use at least one IG FinFET. The backgate of an IG pFinFET (nFinFET) is tied to a () signal. When these signals are reversebiased, for example, when is 0.2 V above and is 0.2 V below ground, there is a significant reduction in . The presence of an IG FinFET also leads to a more complex layout, resulting in 36% area overhead relative to that of an 2 SG INV (that is double the size of a minimumsized SG INV). Table 2 compares the normalized area, delay, and leakage of the various INVs. Clearly, SG INV is the best in area and propagation delay (), but incurs much higher leakage current than LP INV. However, LP INV performs poorly in area and propagation delay. IGn INV, however, looks promising based on its intermediate area, delay, and leakage.

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4.2. SG/IG NAND2
Similar to INVs, NAND2 gates also have SG (LP) configurations in which all transistors are SG (IG) FinFETs. Since there are more transistors in a NAND2 gate than in an INV, there are more opportunities available for combining SG and IG FinFETs. This leads to various other configurations: MT, IG, IG2, XT, and XT2. Schematic diagrams of SG, LP, and MT NAND2 gates are shown in Figure 19. Schematic diagrams for IG, IG2, XT, and XT2 NAND2 gates are shown in Figure 20. Table 3 shows the normalized area, delay, and leakage of all these NAND2 gates. Again, all comparisons in Table 3 are made relative to 2 SG NAND2 gate, because it is the largest SG NAND2 gate that can be accommodated in the standard cell height. SG NAND2 outperforms others in area and propagation delay, but consumes significantly more leakage current than LP NAND2. Out of all the variants, XT2 NAND2 stands out as a reasonable compromise.

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4.3. ASG Logic Gates
Bhoj and Jha investigated INV and NAND2 gates with a mix of SG and ASG FinFETs [49]. Schematics/layouts of any SGFinFET logic gate can be converted to those of an ASGFinFET logic gate, as shown in Figure 21, without any area overhead. Hence, introduction of ASG FinFETs only impacts leakage and propagation delay. Preserving some of the SG FinFETs in the NAND2S gate (Figure 21(c)) enables leakagedelay tradeoffs, as evident from the leakagedelay spectrum shown in Figure 22 for various logic gates. The pure ASG gates lie in the left half of the spectrum, indicating low leakage, while pure SG gates lie in the bottom half of the spectrum, indicating less delay.
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4.4. SG/IG/ASG Latches and FlipFlops
Bruteforce transmission gate (TG) and halfswing (HS) latches and flipflops (as shown in Figures 23 and 24) implemented with SG, IG, and ASG FinFETs have also been investigated [49, 50]. Tawfik et al. proposed an IG latch by introducing IG FinFETs in the feedback inverter (I3) of the allSG TG latch in Figure 23(a). With appropriate reversebiasing of the back gates, the IG FinFETs in I3 are made weaker compared to the drive inverter (I1). As a result, the drive inverter need not be oversized, as conventionally done, ensuring a safe write operation at the same time. At nominal process corners, the IG latch leads to 33% less leakage power and 20% less area compared to the conventional SG latch with almost no degradation in propagation delay and setup time. Similar power and area improvements are obtained for IG flipflops relative to TG flipflops (Figure 24(a)). Bhoj and Jha introduced ASG FinFETs in the TG and HS latches and observed similar tradeoffs. Introducing ASG FinFETs in all the latch inverters (I1, I2, and I3) results in a minimumleakage and maximumdelay configuration. Introducing ASG FinFETs in only I3 leads to a configuration similar to the IG latch. The new configuration reduces leakage power by approximately 50%, but the propagation delay increases by roughly 30%. This configuration also results in area savings as I1 can be sized down, maintaining the desired write stability. Similar results are obtained for ASG flipflops as well.
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As in the case of TG latches and flipflops, combinations of SG, IG, and ASG FinFETs in inverters (I1 and I2) and nFinFETs (N1 to N4) generate various HS latches (Figure 23(b)) and flipflops (Figure 24(b)). As expected, the leakage power of the allASG configuration is reduced by almost 65%, however, at the expense of doubling of its propagation delay. Using ASG FinFETs in N2/N4 only makes an interesting configuration that results in around 20% improvement in leakage, but only at a negligible cost (less than 5%) in propagation delay. Similar results were obtained for HS flipflops.
4.5. SRAM
SRAM is a key component of onchip caches of stateoftheart microprocessors. In today’s multicore processors, typically more than half of the die area is dedicated to SRAMs [106]. Since SRAMs are built with the smallest transistors possible at a technology node (in order to increase the memory density), statistical fluctuations are extremely detrimental to SRAM performance. Deeply scaled SRAMs, built atop planar MOSFETs, suffer from mismatches in transistor strengths and caused by RDF and other sources of process variations. SRAMs also consume most of the chip’s total leakage power because of very long idle periods in large memory arrays. Sixtransistor (6T) FinFET SRAMs (as shown in Figure 25) have been explored quite thoroughly in the past decade from the point of view of suppressing leakage power and tackling increased variability among bitcells [52–60, 64, 65]. Figure 26 shows the butterfly curves, under process variations, for MOSFET and FinFET based SRAMs. The curves clearly demonstrate that FinFET SRAMs have a superior static noise margin (SNM) because they do not suffer from RDF.
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New SRAM bitcell structures have been proposed using a mix of SG, IG, and ASG FinFETs [55, 56, 60, 62]. In [55], FinFET SRAMs have been classified into the following categories: (i) vanilla shortedgate configurations (VSCs) in which all FinFETs are SG, (ii) independentgate configurations (IGCs) in which one or more SG FinFETs are replaced with IG FinFETs, and (iii) multiple workfunction shortedgate configurations (MSCs) in which one or more SG FinFETs are replaced with ASG FinFETs. Table 4 shows the best bitcells from the perspectives of different metrics. , , , , , and refer to the read power noise margin, writetrip power, read current, leakage current, read access time, and write access time of the bitcell, respectively. Out of these, and represent transient metrics whereas the remaining metrics are DC. In Table 4, and refer to VSC and MSC bitcells that have , , and fins in the pullup (PU), passgate (PG), and pulldown (PD) FinFETs, respectively. Passgate feedback (PGFB) [59], pullup write gating (PUWG) [60], split pullup (SPU) [65], and rowbased backgate bias (RBB) [64] are some popular IGC FinFET SRAM bitcells, as shown in Figure 27. Table 4 also indicates that there is no single SRAM cell that is the best in all the metrics, but it is possible to find a cell that is ahead of the others in some of the metrics. A careful look at the absolute values of the metrics reveals that IGC bitcells exhibit superior DC metric values relative to those of VSC bitcells, but their poor transient performance makes them unattractive. On the other hand, MSC bitcells have competitive DC metric values and better transient performance relative to VSC bitcells. Hence, in a nutshell, MSC bitcells may be a good choice for a FinFET SRAM bitcell. Out of all MSC bitcells, A(111) seems to be the most promising one. It is also shown in [55] that the transient behavior of a bitcell is very important to account for. Evaluations based on only DC metrics may lead to incorrect conclusions.

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Goel et al. proposed a different FinFET SRAM bitcell using ADSE FinFETs in the access transistors (i.e., the PG FinFETs) [57]. When the extended spacers of the PG FinFETs are placed towards the internal storage nodes (NL and NR) of the bitcell, it is called contactunderlapstorage (CUS) SRAM. This SRAM exploits the bidirectional current flow in ADSE FinFETs to improve both the read and write margins (by 11% and 6%, resp.). Also, it reduces the leakage current by as much as 57%. However, it suffers from a degraded access time (7%) and cell area (7%).
Moradi et al. proposed a FinFET SRAM bitcell that exploits AD FinFETs [58]. The lowly doped drains of the ADaccess transistors are placed towards the storage nodes. This SRAM bitcell is able to resolve the readwrite conflicts as the strength of the access transistors varies based on the voltage of the storage nodes. This boosts both read (7.3%) and write (23%) margins. These improvements come at the cost of an increased access time (42%) because the access transistor becomes weak during a read operation. Improvement in subthreshold leakage of this bitcell is also to be noted (2.8).
Sachid and Hu showed that multiple finheight FinFETs can be used to design more dense and stable SRAMs [61]. Using multiple fin heights enables better control over the strengths of PU, PG, and PD transistors, leading to a better noise margin, without incurring any area penalty. The drawbacks of this scheme are increased leakage power and process complexity.
4.6. DRAM
Onetransistor dynamic randomaccess memories (1TDRAMs) have traditionally been used both in offchip main memory and onchip caches due to their significant area advantage over SRAMs. With the advent of partially depletedSOI (PDSOI) technology, a capacitorless 1TDRAM, also known as floatingbody cell (FBC), was proposed. This DRAM leads to a smaller area and a less complicated fabrication process than conventional embedded DRAMs [107–109]. Its functionality is based on the shift produced by majority carrier accumulation in the floating body. However, the higher impurity concentration required to suppress SCEs degrades the retention characteristics of planar PDSOI 1TDRAMs. Doublegate FinFET DRAMs (DG FinDRAM) are able to overcome these scaling issues of 1TDRAMs [110–112]. The second gate, with the application of an appropriate bias, helps with the accumulation of majority carriers and thereby relaxes the high impurity concentration criterion. FinFET based 1TDRAMs also exhibit long retention times and large sense margins. Thus, they have emerged as a promising embedded memory alternative.
5. CircuitLevel Analysis
Logic circuit analysis and optimization tools have been implemented using FinFET based standard cell libraries described in the previous section. In this section, we describe them in brief.
5.1. Analysis
FinPrin is a statistical static timing analysis (SSTA) and power analysis tool for FinFET logic circuits that considers PVT variations and is based on accurate statistical models for delay, dynamic power, and leakage power of the logic netlist [113]. It takes a register transferlevel (RTL) or gatelevel description of a netlist as an input and estimates leakage/dynamic power and delay distributions ( and for Gaussian distributions) at every node of the netlist, based on the circuitlevel parameter values provided in the FinFET design library, such as input and output capacitance, input and output resistance, and leakage current, taking into account the impact of PVT variations. The leakage and temperature variation models are macromodel based [94], whereas the delay models are based on an SSTA approach [114]. These models also take spatial correlations of the gates into account using a rectangular grid based method [115]. FinPrin’s performance has been compared with that of accurate quasiMonte Carlo (QMC) simulations [116, 117] and was shown to produce very accurate means () and reasonably accurate standard deviations (), while enabling a significant computation time speedup (two orders of magnitude).
5.2. Optimization
Optimization of logic circuits is made possible by accurate analysis. Synopsys Design Compiler is commercially used for power/delay optimization of logic circuits, given a standard cell library [99]. In order to exploit the various FinFET design styles, a linear programming based optimization algorithm and tool are proposed in [39]. The algorithm is used to assign gate sizes and FinFET types to the mapped circuit, under a timing constraint, by selecting standard cells from the FinFET design library. Unlike traditional greedy gatesizing algorithms, this algorithm divides the available slack among gates whose cells may be replaced. It is shown that this approach can achieve 15–30% better power consumption than Synopsys Design Compiler [39].
5.3. Novel Interconnect Structures and Logic Synthesis
Interconnects assume a lot of importance in deeply scaled technology nodes as they govern the delay and power consumption of modern integrated circuits. FinFETs not only provide newer circuit design styles, but also can lead to an efficient interconnect implementation strategy. A mechanism to improve the interconnect efficiency, called threshold voltage control through multiple supply voltages (TCMS), has been proposed in [34]. The TCMS principle is based on the fact that the backgate bias of a FinFET affects the of the front gate. Instead of using the conventional dual scheme, TCMS uses a slightly higher supply voltage () and a slightly negative supply voltage () along with the nominal supply voltages, , and ground (which is referred to as for symmetry). TCMS is based on the observation that an overdriven inverter (i.e., whose input is driven by an inverter supplied with and and whose supply voltage is ), as shown in Figure 28, has both less leakage and less delay. Less leakage is ensured because of an increase in the of the leaking transistor and less delay is ensured because of the higher current drive in the active transistor. The improvement in the drive strength of the active transistor results in improved delay that can be traded off for area and power reduction under a given timing constraint. A chain of such inverter pairs can be formed on the interconnect, as shown in Figure 29, without the need for voltagelevel shifters due to the use of higher transistors in the inverter supplied with and . This scheme enables a significant reduction in subthreshold leakage power in TCMS buffer interconnects. It has been shown that, on an average, TCMS provides overall power savings of 50.4% along with area savings of 9.2% as compared to a stateoftheart dual interconnect synthesis scheme [34].
The TCMS principle can also be applied to logic synthesis [35]. In this case, a FinFET logic gate can take advantage of the TCMS principle if its input arrives from a gate supplied with the set and its supply voltage belongs to the set. Since the opposite scenario leads to a high leakage current, it is avoided. Based on the combinations of supply voltage ( or ), input voltage ( or ), and threshold voltage (high or low), INV and NAND2 have seven and 25 variants, respectively. As in the case of the interconnects, use of high FinFETs in gates that need to be driven by a input voltage obviates the need for a voltagelevel converter between the and gates. With the use of a linear programming based optimization algorithm, TCMS leads to an overall power reduction of 3 under relaxed delay constraints.
6. ArchitectureLevel Analysis
Next, we ascend the design hierarchy to the architecture level. Due to shrinking feature sizes and severe process variations, the delay and power consumption at the chip level are not easy to predict any more [114]. Because of their inherent statistical nature, a yield analysis of an integrated circuit (under a design constraint) has become very important. This analysis estimates the percentage of chips that will meet the given power and delay constraints for the particular chip architecture for a given process. In the following subsections, we discuss PVTaware simulation tools for various FinFET based architectural components.
6.1. FinFET Based Caches
An integrated PVT variationaware powerdelay simulation framework, called FinCANON [69], has been developed for FinFET based caches and NoCs. It has two components: CACTIPVT for caches and ORIONPVT for NoCs. CACTIPVT is an extension of CACTFinFET [67]. CACTIPVT can be used to obtain the delay and leakage distributions of FinFET based caches with varying sizes, SRAM cell types, and backgate biases. The block diagram of CACTIPVT is shown in Figure 30. It uses a FinFET design library consisting of FinFET logic gates of various sizes and types and different types of FinFET SRAM cells. This library is characterized using accurate device simulation. The process variation models used in CACTIPVT are calibrated using QMC simulations, along with the rectangular gridbased method to model spatial correlations. Peripheral components implemented with SG FinFETs and SRAM cells implemented with some IG FinFETs or ASG FinFETs provide the best balance between delay and leakage of the FinFET caches.
6.2. FinFET Based NoCs
With increasing number of cores in chip multiprocessors (CMPs), NoCs have emerged as an effective communication mechanism among the cores. FinCANON also includes a performance/power simulation tool, called ORIONPVT, aimed at FinFET NoCs [69]. ORIONPVT, whose block diagram is shown in Figure 31, is an extension of ORIONFinFET [68]. Here, an SSTA technique and a macromodel based methodology are used to model the PVT variations in delay and leakage. It also provides a power breakdown of an onchip router. Leakage power is found to dominate the total power of the router at higher temperatures.
A FinFET based implementation of a variablepipelinestage router (VPSR) is proposed in [70]. VPSR enables dynamic adjustment of the number of pipeline stages in the router based on incoming network traffic. As a result, different flow control digits (flits) may traverse pipeline stages of varying lengths while passing through the router. This leads to enhanced router performance because VPSR adapts its throughput to the network traffic requirement at runtime. VPSR also enables significant savings in leakage power through reversebiasing (called adaptive backgate biasing) of the back gates of IG FinFETs in infrequently accessed components of the router.
6.3. FinFET Based Multicore Processors
In the computer architecture domain, the trend has shifted in recent years from uniprocessors to CMPs and multicore systems in order to serve the everincreasing performance demand. Tools like FinCANON have paved the way for a more powerful tool for characterizing multicore processors. McPATPVT is a PVT variationaware integrated powerdelay simulation tool for FinFET based multicore processors [71]. Figure 32 shows the block diagram of McPATPVT. It has two key components: processor model and yield analyzer. The processor model contains power/delay macromodels of various functional units (e.g., arithmeticlogic unit, floatingpoint unit, memory management unit, etc.) of the processor core. The yield analyzer can predict the yield of a specified processor configuration under PVT variations. Figure 33 zooms into the components of the processor model. The efficacy of this tool has been demonstrated on an alphalike processor core and multicore simulations based on Princeton Application Repository for SharedMemory Computer (PARSEC) benchmarks.
7. Conclusion
In this paper, we have explored the impact of FinFETs from the device to architecture level. We learnt about the shortcomings of planar MOSFETs in today’s deeply scaled technologies and the advantages of FinFETs as suitable replacements for planar MOSFETs. We looked into FinFET device characteristics and evaluated tradeoffs among SG, IG, and ASG FinFETs, along with other FinFET asymmetries, such as drainspacer extension, source/drain doping, gateoxide thickness, and fin height. We learnt about the detrimental impact of PVT variations on FinFET chip performance and power. We surveyed techniques for characterizing FinFET devices and circuits and explored FinFET based logic gates, flipflops, and memory cells. Finally, we also reviewed PVT variationaware FinFET circuit and architecturelevel simulation tools. We observed leakagedelay tradeoffs that are possible at each level of the design hierarchy. The availability of a plethora of FinFET styles opens up new design opportunities at each level, which we hope some of the readers will be willing to explore.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgment
This work was supported by NSF under Grant nos. CCF1217076 and CCF1318603.
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Copyright © 2014 Debajit Bhattacharya and Niraj K. Jha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.