Arithmetic logic UNIT (ALU) design using reconfigurable CMOS logic

Using the reconfigurable logic of multi-input floating gate MOSFETs, a 4-bit ALU has been designed for 3V operation. The ALU can perform four arithmetic and four logical operations. Multi- input floating gate (MIFG) transistors have been promising in realizing increased functionality on a chip. A multi- input floating gate MOS transistor accepts multiple inputs signals, calculates the weighted sum of all input signals and then controls the ON and OFF states of the transistor. This enhances the transistor function to more than just switching. This changes the way a logic function can be realized. Implementing a design using multi-input floating gate MOSFETs brings about reduction in transis tor count and number of interconnections. The advantage of bringing down the number of devices is that a design becomes area efficient and power consumption reduces. There are several applications that stress on smaller chip area and reduced power. Multi- input floating gate devices have their use in memories, analog and digital circuits. In the present work we have shown successful implementation of multi- input floating gate MOSFETs in ALU design. A comparison has been made between adders using different design methods w.r.t transistor count. It is seen that our design, implemented using multi-input floating gate MOSFETs, uses the least number of transistors when compared to other designs. The design was fabricated using double polysilicon standard CMOS process by MOSIS in 1.5mm technology. The experimental waveforms and delay measurements have also been presented

Ternary to binary converter design in CMOS using multiple input floating gate MOSFETS

In this work, a ternary to binary converter circuit is designed in 0.5μm n-well CMOS technology. The circuit takes two inputs corresponding to the ternary bits and gives four outputs, which are the binary equivalent bits of the ternary inputs. The ternary inputs range from (-1,-1)3 to (1,1) 3 which are decimal -4 to 4 and the four binary output bits are the sign bit (SB), most significant bit (MSB), second significant bit (SSB) and the least significant bit (LSB). The ternary inputs (-1, 0 and 1) are represented in terms of voltages of -3V, 0V and 3V. Multiple input floating gate (MIFG) MOSFETS are used in the design of ternary to binary converter. The four circuits to generate the SB, MSB, SSB and LSB outputs are designed separately and then connected together to perform the entire conversion. The MIFG MOSFET takes multiple input signals, which are the ternary inputs in this case and calculates the weighted sum of the inputs. This weighted sum of the inputs is called floating gate voltage and is given as input to the CMOS inverter. The CMOS inverter gives a high or low binary output depending on if the floating gate voltage is higher or lower than the threshold voltage of the CMOS inverter. The circuits are simulated using MOSIS BSIM level 7 model parameters. LEDIT version 13 is used for the layout and a total of 22 transistors are used in the design of the converter circuit. The floating gate of the transistor is simulated by not giving the input directly to the gate of the transistor. Instead inputs are fed to one end of the capacitors and the other end of the capacitors are tied together and given as an input to the inverter. The converter chip occupies an area of 1140 × 2090 μm2

Improving the Immunity of Hybrid SET/MOS Circuits Using Boltzmann Machine Network

Rapid progress in the fabrication technology of silicon nano devices has pushed the device dimension toward 1- 100nm length scale, which renders the basic working principles of CMOS devices more dependent upon quantum effects and doping fluctuations. When device dimensions are scaled down to a few nanometers, quantum effects such as single electron tunneling (SET) and energy quantization lead to interesting new device characteristics that can be exploited to create extremely compact circuits. The SET is one type of nanoscale electronic devices based on quantum tunneling and Coulomb blockade effect, where one or more Coulomb islands are sandwiched between two tunnel junctions which connect respectively with the drain electrode and the source electrode, and are capacitively coupled with one or more gate electrodes. However, both pure SET devices and hybrid SET-MOS circuits face a big problem – the background charges, which influence the accuracy of the circuit. In order to improve their immunity against these charges, we introduce the neuron network ‘Boltzmann machine’ into the circuit. This idea is to improve the accuracy with increasing time redundancy. Single-electron circuits show stochastic behaviors in their operation because of the probabilistic nature of electron tunneling phenomena. They can therefore be successfully used for implementing the stochastic neuron operation of Boltzmann machines. This thesis proposes applications of Boltzmann machine network to improve the immunity of hybrid SET/MOS circuits to overcome random background charges. Detailed unit neuron block and whole neuron network model are used to design hybrid SET/MOS circuits. Two applications based on Boltzmann machine are proposed: (1) Multi-bit A/D converter, and (2) One-bit full adder. Simulation was done using Cadence Spectre simulator with 180nm CMOS model and SET MIB macro model for performance evaluation. And it is expected that our idea can be extended to other hybrid SETMOS

Potential and Challenges of Analog Reconfigurable Computation in Modern and Future CMOS

In this work, the feasibility of the floating-gate technology in analog computing platforms in a scaled down general-purpose CMOS technology is considered. When the technology is scaled down the performance of analog circuits tends to get worse because the process parameters are optimized for digital transistors and the scaling involves the reduction of supply voltages. Generally, the challenge in analog circuit design is that all salient design metrics such as power, area, bandwidth and accuracy are interrelated. Furthermore, poor flexibility, i.e. lack of reconfigurability, the reuse of IP etc., can be considered the most severe weakness of analog hardware. On this account, digital calibration schemes are often required for improved performance or yield enhancement, whereas high flexibility/reconfigurability can not be easily achieved. Here, it is discussed whether it is possible to work around these obstacles by using floating-gate transistors (FGTs), and analyze problems associated with the practical implementation. FGT technology is attractive because it is electrically programmable and also features a charge-based built-in non-volatile memory. Apart from being ideal for canceling the circuit non-idealities due to process variations, the FGTs can also be used as computational or adaptive elements in analog circuits. The nominal gate oxide thickness in the deep sub-micron (DSM) processes is too thin to support robust charge retention and consequently the FGT becomes leaky. In principle, non-leaky FGTs can be implemented in a scaled down process without any special masks by using “double”-oxide transistors intended for providing devices that operate with higher supply voltages than general purpose devices. However, in practice the technology scaling poses several challenges which are addressed in this thesis. To provide a sufficiently wide-ranging survey, six prototype chips with varying complexity were implemented in four different DSM process nodes and investigated from this perspective. The focus is on non-leaky FGTs, but the presented autozeroing floating-gate amplifier (AFGA) demonstrates that leaky FGTs may also find a use. The simplest test structures contain only a few transistors, whereas the most complex experimental chip is an implementation of a spiking neural network (SNN) which comprises thousands of active and passive devices. More precisely, it is a fully connected (256 FGT synapses) two-layer spiking neural network (SNN), where the adaptive properties of FGT are taken advantage of. A compact realization of Spike Timing Dependent Plasticity (STDP) within the SNN is one of the key contributions of this thesis. Finally, the considerations in this thesis extend beyond CMOS to emerging nanodevices. To this end, one promising emerging nanoscale circuit element - memristor - is reviewed and its applicability for analog processing is considered. Furthermore, it is discussed how the FGT technology can be used to prototype computation paradigms compatible with these emerging two-terminal nanoscale devices in a mature and widely available CMOS technology.Siirretty Doriast

First order sigma-delta modulator of an oversampling ADC design in CMOS using floating gate MOSFETS

We report a new architecture for a sigma-delta oversampling analog-to-digital converter (ADC) in which the first order modulator is realized using the floating gate MOSFETs at the input stage of an integrator and the comparator. The first order modulator is designed using an 8 MHz sampling clock frequency and implemented in a standard 1.5µm n-well CMOS process. The decimator is an off-chip sinc-filter and is programmed using the VERILOG and tested with Altera Flex EPF10K70RC240 FPGA board. The ADC gives an 8-bit resolution with a 65 kHz bandwidth

Novel High Performance Ultra Low Power Static Random Access Memories (SRAMs) Based on Next Generation Technologies

Title from PDF of title page viewed January 27, 2021Dissertation advisor: Masud H. ChowdhuryVitaIncludes bibliographical references (page 107-120)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2019Next Big Thing Is Surely Small: Nanotechnology Can Bring Revolution. Nanotechnology leads the world towards many new applications in various fields of computing, communication, defense, entertainment, medical, renewable energy and environment. These nanotechnology applications require an energy-efficient memory system to compute and process. Among all the memories, Static Random Access Memories (SRAMs) are high performance memories and occupies more than 50% of any design area. Therefore, it is critical to design high performance and energy-efficient SRAM design. Ultra low power and high speed applications require a new generation memory capable of operating at low power as well as low execution time. In this thesis, a novel 8T SRAM design is proposed that offers significantly faster access time and lowers energy consumption along with better read stability and write ability. The proposed design can be used in the conventional SRAM as well as in computationally intensive applications like neural networks and machine learning classifiers [1]-[4]. Novel 8T SRAM design offers higher energy efficiency, reliability, robustness and performance compared to the standard 6T and other existing 8T and 9T designs. It offers the advantages of a 10T SRAM without the additional area, delay and power overheads of the 10T SRAM. The proposed 8T SRAM would be able to overcome many other limitations of the conventional 6T and other 7T, 8T and 9T designs. The design employs single bitline for the write operation, therefore the number of write drivers are reduced. The defining feature of the proposed 8T SRAM is its hybrid design, which is the combination of two techniques: (i) the utilization of single-ended bitline and (ii) the utilization of virtual ground. The single-ended bitline technique ensures separate read and write operations, which eventually reduces the delay and power consumption during the read and write operations. It's independent read and write paths allow the use of the minimum sized access transistors and aid in a disturb-free read operation. The virtual ground weakens the positive feedback in the SRAM cell and improves its write ability. The virtual ground technique is also used to reduce leakages. The proposed design does not require precharging the bitlines for the read operation, which reduces the area and power overheads of the memory system by eliminating the precharging circuit. The design isolates the storage node from the read path, which improves the read stability. For reliability study, we have investigated the static noise margin (SNM) of the proposed 8T SRAM, for which, we have used two methods – (i) the traditional SNM method with the butterfly curve, (ii) the N-curve method A comparative analysis is performed between the proposed and the existing SRAM designs in terms of area, total power consumption during the read and write operations, and stability and reliability. All these advantages make the proposed 8T SRAM design an ideal candidate for the conventional and computationally intensive applications like machine learning classifier and deep learning neural network. In addition to this, there is need for next generation technologies to design SRAM memory because the conventional CMOS technology is approaching its physical and performance boundaries and as a consequence, becoming incompatible with ultra-low-power applications. Emerging devices such as Tunnel Field Effect Transistor (TFET)) and Graphene Nanoribbon Field Effect Transistor (GNRFET) devices are highly potential candidates to overcome the limitations of MOSFET because of their ability to achieve subthreshold slopes below 60 mV/decade and very low leakage currents [6]-[9]. This research also explores novel TFET and GNRFET based 6T SRAM. The thesis evaluates the standby leakage power in the Tunnel FET (TFET) based 6T SRAM cell for different pull-up, pull-down, and pass-gate transistors ratios (PU: PD: PG) and compared to 10nm FinFET based 6T SRAM designs. It is observed that the 10nm TFET based SRAMs have 107.57%, 163.64%, and 140.44% less standby leakage power compared to the 10nm FinFET based SRAMs when the PU: PD: PG ratios are 1:1:1, 1:5:2 and 2:5:2, respectively. The thesis also presents an analysis of the stability and reliability of sub-10nm TFET based 6T SRAM circuit with a reduced supply voltage of 500mV. The static noise margin (SNM), which is a critical measure of SRAM stability and reliability, is determined for hold, read and write operations of the 6T TFET SRAM cell. The robustness of the optimized TFET based 6T SRAM circuit is also evaluated at different supply voltages. Simulations were done in HSPICE and Cadence tools. From the analysis, it is clear that the main advantage of the TFET based SRAM would be the significant improvement in terms of leakage or standby power consumption. Compared to the FinFET based SRAM the standby leakage power of the T-SRAMs are 107.57%, 163.64%, and 140.44% less for 1:1:1, 1:5:2 and 2:5:2 configurations, respectively. Since leakage/standby power is the primary source of power consumption in the SRAM, and the overall system energy efficiency depends on SRAM power consumption, TFET based SRAM would lead to massive improvement of the energy efficiency of the system. Therefore, T-SRAMs are more suitable for ultra-low power applications. In addition to this, the thesis evaluates the standby leakage power of types of Graphene Nanoribbon FETs based 6T SRAM bitcell and compared to 10nm FinFET based 6T SRAM bitcell. It is observed that the 10nm MOS type GNRFET based SRAMs have 16.43 times less standby leakage power compared to the 10nm FinFET based SRAMs. The double gate SB-GNRFET based SRAM consumes 1.35E+03 times less energy compared to the 10nm FinFET based SRAM during write. However, during read double gate SB-GNRFET based SRAM consume 15 times more energy than FinFET based SRAM. It is also observed that GNRFET based SRAMs are more stable and reliable than FinFET based SRAM.Introduction -- Background -- Novel High Performance Ultra Low Power SRAM Design -- Tunnel FET Based SRAM Design -- Graphene Nanoribbon FET Based SRAM Design -- Double-gate FDSOI Based SRAM Designs -- Novel CNTFET and MEMRISTOR Based Digital Designs -- Conclusio