Electronic and photonic technologies have transformed our lives-from computing and mobile devices, to information technology and the internet. Our future demands in these fields require innovation in each technology separately, but also depend on our ability to harness their complementary physics through integrated solutions1,2. This goal is hindered by the fact that most silicon nanotechnologies-which enable our processors, computer memory, communications chips and image sensors-rely on bulk silicon substrates, a cost-effective solution with an abundant supply chain, but with substantial limitations for the integration of photonic functions. Here we introduce photonics into bulk silicon complementary metal-oxide-semiconductor (CMOS) chips using a layer of polycrystalline silicon deposited on silicon oxide (glass) islands fabricated alongside transistors. We use this single deposited layer to realize optical waveguides and resonators, high-speed optical modulators and sensitive avalanche photodetectors. We integrated this photonic platform with a 65-nanometre-transistor bulk CMOS process technology inside a 300-millimetre-diameter-wafer microelectronics foundry. We then implemented integrated high-speed optical transceivers in this platform that operate at ten gigabits per second, composed of millions of transistors, and arrayed on a single optical bus for wavelength division multiplexing, to address the demand for high-bandwidth optical interconnects in data centres and high-performance computing3,4. By decoupling the formation of photonic devices from that of transistors, this integration approach can achieve many of the goals of multi-chip solutions 5 , but with the performance, complexity and scalability of 'systems on a chip'1,6-8. As transistors smaller than ten nanometres across become commercially available 9 , and as new nanotechnologies emerge10,11, this approach could provide a way to integrate photonics with state-of-the-art nanoelectronics.

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Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip.

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 short-channel 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 logic-level and architecture-level tradeoffs offered by
FinFETs. We also review analysis and optimization tools that
are available for characterizing FinFET devices, circuits, and
architectures.

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FinFETs: From Devices to Architectures

This paper presents the implementation details and silicon results of a 3 GHz dual-core ARM Cortex TM -A9 (A9) manufactured in the 28 nm planar Ultra-Thin Box and Body Fully-Depleted CMOS (UTBB FD-SOI) technology. The implementation is based on a fully synthesizable standard design flow. The design exploits the important flexibility provided by the FD-SOI technology, notably a wide Dynamic Voltage and Frequency Scaling (DVFS) range, from 0.52 V to 1.37 V, and Forward Body Bias (FBB) techniques up to 1.3 V. Detailed explanations of the body-biasing techniques specific to this technology are largely presented, in the context of a multi- VT co-integration, which enable this energy efficient silicon implementation. The system integrates all the advanced IPs for energy efficiency as well as the body bias generator and a fast (μs range) dynamic body bias management capability. The measured dual core CPU maximum operation frequency is 3 GHz (for 1.37 V) and it can be operated down to 300 MHz (for 0.52 V) in full continuous DVFS. The obtained relative performance, with respect to an equivalent planar 28 nm bulk CMOS chip, shows an improvement of +237% at 0.6 V, or +544% at 0.61 V with 1.3 V FBB.

A 3 GHz Dual Core Processor ARM Cortex TM -A9 in 28 nm UTBB FD-SOI CMOS With Ultra-Wide Voltage Range and Energy Efficiency Optimization

We report record contact resistance and transconductance in locally back-gated black phosphorus p-MOSFETs with 7-nm thick HfO2 gate dielectrics. Devices with effective gate lengths, $L_{\mathrm {\mathbf {eff}}}$ , from 0.55 to 0.17 $\mu \text{m}$ were characterized and shown to have contact resistance values as low as $1.14~\pm ~0.05~\Omega $ -mm. In addition, devices with $L_{\mathrm {\mathbf {eff}}}= 0.17~\mu \text{m}$ displayed extrinsic transconductance exceeding 250 $\mu \text{S}$ / $\mu \text{m}$ and ON-state current approaching $300~\mu \text{A}$ / $\mu \text{m}$ .

Black Phosphorus p-MOSFETs With 7-nm HfO 2 Gate Dielectric and Low Contact Resistance

This brief proposes a novel power-gated 9T (PG9T) static random access memory (SRAM) cell that uses a read-decoupled access buffer and power-gating transistors to execute reliable read and write operations. The proposed 9T SRAM cell uses bit interleaving to achieve soft error immunity and utilizes a column-based virtual $V_{\mathrm {\mathbf {SS}}}$ signal to eliminate unnecessary bitline discharges in the unselected columns, thereby reducing the energy consumption. In a 22-nm FinFET technology, the proposed PG9T SRAM cell has a minimum operating voltage of 0.32 V while achieving the $6\sigma $ read stability yield. Compared with the previously proposed 9T SRAM cell, the proposed cell consumes 45% and 17% less energy per read and write operation, respectively, at the minimum operating voltage, and has a 12% smaller bit cell area.

Power-Gated 9T SRAM Cell for Low-Energy Operation

In this paper, the design space, including fin thickness (Tfin), fin height (Hfin), fin ratio of bit-cell transistors, and surface orientation, is researched to optimize the stability, leakage current, array dynamic energy, and read/write delay of the FinFET SRAM under layout area constraints. The simulation results, which consider the variations of both Tfin and threshold voltage (Vth), show that most FinFET SRAM configurations achieve a superior read/write noise margin when compared with planar SRAMs. However, when two fins are used as pass gate transistors (PG) in FinFET SRAMs, enormous array dynamic energy is required due to the increased effective gate and drain capacitance. On the other hand, a FinFET SRAM with a one-fin PG in the (110) plane shows a smaller write noise margin than the planar SRAM. Thus, the one-fin PG in the (100) plane is suitable for FinFET SRAM design. The one-fin PG FinFET SRAM with Tfin = 10 nm and Hfin = 40 nm in the (100) plane achieves a three times larger noise margin when compared with the planar SRAM and consumes a 17% smaller bit-line toggling array energy at a cost of a 22% larger word-line toggling energy. It also achieves a 2.3 times smaller read delay and a 30% smaller write delay when compared with the planar SRAM.

FinFET SRAM Optimization With Fin Thickness and Surface Orientation

Stable SRAM cells utilizing Independent Gate FinFET architectures provide improvements over conventional SRAM cells in device parameters such as Read Static Noise Margin (RSNM) and Write Noise Margin (WNM). Exemplary SRAM cells comprise a pair of storage nodes, a pair of bit lines, a pair of pull-up devices, a pair of pull-down devices and a pair of pass-gate devices. A first control signal and a second control signal are configured to adjust drive strengths of the pass-gate devices, and a third control signal is configured to adjust drive strengths of the pull-up devices, wherein the first control signal is routed orthogonal to a bit line direction, and the second and third control signals are routed in a direction same as the bit line direction. RSNM and WNM are improved by adjusting drive strengths of the pull-up and pass-gate devices during read and write operations.

Stable SRAM Bitcell Design Utilizing Independent Gate Finfet

With newer technology nodes, circuit/device/process codesign is essential to realize the advantages of scaling. Leveraging co-design approach based on a well-established manufacturing flow, a cost effective 28 nm 4G SOC technology has been crafted. This 28 nm design strategy uses two sets of design rules and 7 different Vt cells with optimal power gating to achieve a 2.4× increase in gate density, 55% decrease in power and 30% gain in frequency with respect to the 45 nm counterpart. Relevant technical tradeoffs between the design/technology interactions are discussed to illustrate the codesign aspects.

Cost effective 28nm LP SoC technology optimized with circuit/device/process co-design for smart mobile devices

A 5 Transistor Static Random Access Memory (5T SRAM) is designed for reduced cell size and immunity to process variation. The 5T SRAM (400) includes a storage element (402) for storing data, wherein the storage element is coupled a first voltage and a ground voltage. The storage element can include symmetrically sized cross - coupled inverters. A single access transistor (M5 ) controls read and write operations on the storage element (402). Control logic (M6,M6') is configured to generate a value of the first voltage for a write operation that is different from the value of the first voltage for a read operation.

Low-power 5t sram with improved stability and reduced bitcell size

SRAM read current tail distribution beyond 6s was studied using Voltage Acceleration Method (VAM). For the first time, non-Gaussian distribution of SRAM and ROM read current was confirmed with direct measurements on actual silicon. Data shows that conventional assumption of Gaussian distribution in read current is inaccurate especially at low Vdd and cold temperature conditions for low power memory in 28nm and beyond technology nodes. In 28nm, this inaccuracy would lead to 2× bit access delay penalty.

Lixin Ge

Papers

FinFET SRAM Optimization With Fin Thickness and Surface Orientation

Stable SRAM Bitcell Design Utilizing Independent Gate Finfet

Cost effective 28nm LP SoC technology optimized with circuit/device/process co-design for smart mobile devices

Low-power 5t sram with improved stability and reduced bitcell size

Non-Gaussian distribution of SRAM read current and design impact to low power memory using Voltage Acceleration Method