Analog Circuits in Ultra-Deep-Submicron CMOS

Modern and future ultra-deep-submicron (UDSM) technologies introduce several new problems in analog design. Nonlinear output conductance in combination with reduced voltage gain pose limits in linearity of (feedback) circuits. Gate-leakage mismatch exceeds conventional matching tolerances. Increasing area does not improve matching any more, except if higher power consumption is accepted or if active cancellation techniques are used. Another issue is the drop in supply voltages. Operating critical parts at higher supply voltages by exploiting combinations of thin- and thick-oxide transistors can solve this problem. Composite transistors are presented to solve this problem in a practical way. Practical rules of thumb based on measurements are derived for the above phenomena

Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.

The key operation in stochastic neural networks, which have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics, is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. Here we report compact, fast, energy-efficient, and scalable stochastic dot-product circuits based on either passively integrated metal-oxide memristors or embedded floating-gate memories. The circuit's high performance is due to mixed-signal implementation, while the efficient stochastic operation is achieved by utilizing circuit's noise, intrinsic and/or extrinsic to the memory cell array. The dynamic scaling of weights, enabled by analog memory devices, allows for efficient realization of different annealing approaches to improve functionality. The proposed approach is experimentally verified for two representative applications, namely by implementing neural network for solving a four-node graph-partitioning problem, and a Boltzmann machine with 10-input and 8-hidden neurons

Limits on Fundamental Limits to Computation

An indispensable part of our lives, computing has also become essential to industries and governments. Steady improvements in computer hardware have been supported by periodic doubling of transistor densities in integrated circuits over the last fifty years. Such Moore scaling now requires increasingly heroic efforts, stimulating research in alternative hardware and stirring controversy. To help evaluate emerging technologies and enrich our understanding of integrated-circuit scaling, we review fundamental limits to computation: in manufacturing, energy, physical space, design and verification effort, and algorithms. To outline what is achievable in principle and in practice, we recall how some limits were circumvented, compare loose and tight limits. We also point out that engineering difficulties encountered by emerging technologies may indicate yet-unknown limits.Comment: 15 pages, 4 figures, 1 tabl

Equalization of Third-Order Intermodulation Products in Wideband Direct Conversion Receivers

This paper reports a SAW-less direct-conversion receiver which utilizes a mixed-signal feedforward path to regenerate and adaptively cancel IM3 products, thus accomplishing system-level linearization. The receiver system performance is dominated by a custom integrated RF front end implemented in 130-nm CMOS and achieves an uncorrected out-of-band IIP3 of -7.1 dBm under the worst-case UMTS FDD Region 1 blocking specifications. Under IM3 equalization, the receiver achieves an effective IIP3 of +5.3 dBm and meets the UMTS BER sensitivity requirement with 3.7 dB of margin

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Perspective of buried oxide thickness variation on triple metal-gate (TMG) recessed-S/D FD-SOI MOSFET

Recently, Fully-Depleted Silicon on Insulator (FD-SOI) MOSFETs have been accepted as a favourable technology beyond nanometer nodes, and the technique of Recessed-Source/Drain (Re-S/D) has made it more immune in regards of various performance factors. However, the proper selection of Buried-Oxide (BOX) thickness is one of the major challenges in the design of FD-SOI based MOS devices in order to suppress the drain electric penetrations across the BOX interface efficiently. In this work, the effect of BOX thickness on the performance of TMG Re-S/D FD-SOI MOSFET has been presented at 60 nm gate length. The perspective of BOX thickness variation has been analysed on the basis of its surface potential profile and the extraction of the threshold voltage by performing two-dimensional numerical simulations. Moreover, to verify the short channel immunity, the impact of gate length scaling has also been discussed. It is found that the device attains two step-up potential profile with suppressed short channel effects. The outcomes reveal that the Drain Induced Barrier Lowering (DIBL) values are lower among conventional SOI MOSFETs. The device has been designed and simulated by using 2D numerical ATLAS Silvaco TCAD simulator

Yield-driven power-delay-optimal CMOS full-adder design complying with automotive product specifications of PVT variations and NBTI degradations

We present the detailed results of the application of mathematical optimization algorithms to transistor sizing in a full-adder cell design, to obtain the maximum expected fabrication yield. The approach takes into account all the fabrication process parameter variations specified in an industrial PDK, in addition to operating condition range and NBTI aging. The final design solutions present transistor sizing, which depart from intuitive transistor sizing criteria and show dramatic yield improvements, which have been verified by Monte Carlo SPICE analysis

Negative Bias Temperature Instability And Charge Trapping Effects On Analog And Digital Circuit Reliability

Nanoscale p-channel transistors under negative gate bias at an elevated temperature show threshold voltage degradation after a short period of stress time. In addition, nanoscale (45 nm) n-channel transistors using high-k (HfO2) dielectrics to reduce gate leakage power for advanced microprocessors exhibit fast transient charge trapping effect leading to threshold voltage instability and mobility reduction. A simulation methodology to quantify the circuit level degradation subjected to negative bias temperature instability (NBTI) and fast transient charge trapping effect has been developed in this thesis work. Different current mirror and two-stage operation amplifier structures are studied to evaluate the impact of NBTI on CMOS analog circuit performances for nanoscale applications. Fundamental digital circuit such as an eleven-stage ring oscillator has also been evaluated to examine the fast transient charge transient effect of HfO2 high-k transistors on the propagation delay of ring oscillator performance. The preliminary results show that the negative bias temperature instability reduces the bandwidth of CMOS operating amplifiers, but increases the amplifier\u27s voltage gain at mid-frequency range. The transient charge trapping effect increases the propagation delay of ring oscillator. The evaluation methodology developed in this thesis could be extended to study other CMOS device and circuit reliability issues subjected to electrical and temperature stresses