Integrated Circuit Design for Hybrid Optoelectronic Interconnects

This dissertation focuses on high-speed circuit design for the integration of hybrid optoelectronic interconnects. It bridges the gap between electronic circuit design and optical device design by seamlessly incorporating the compact Verilog-A model for optical components into the SPICE-like simulation environment, such as the Cadence design tool. Optical components fabricated in the IME 130nm SOI CMOS process are characterized. Corresponding compact Verilog-A models for Mach-Zehnder modulator (MZM) device are developed. With this approach, electro-optical co-design and hybrid simulation are made possible. The developed optical models are used for analyzing the system-level specifications of an MZM based optoelectronic transceiver link. Link power budgets for NRZ, PAM-4 and PAM-8 signaling modulations are simulated at system-level. The optimal transmitter extinction ratio (ER) is derived based on the required receiver\u27s minimum optical modulation amplitude (OMA). A limiting receiver is fabricated in the IBM 130 nm CMOS process. By side- by-side wire-bonding to a commercial high-speed InGaAs/InP PIN photodiode, we demonstrate that the hybrid optoelectronic limiting receiver can achieve the bit error rate (BER) of 10-12 with a -6.7 dBm sensitivity at 4 Gb/s. A full-rate, 4-channel 29-1 length parallel PRBS is fabricated in the IBM 130 nm SiGe BiCMOS process. Together with a 10 GHz phase locked loop (PLL) designed from system architecture to transistor level design, the PRBS is demonstrated operating at more than 10 Gb/s. Lessons learned from high-speed PCB design, dealing with signal integrity issue regarding to the PCB transmission line are summarized

A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing

Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have molded neural biophysical dynamics but are incompatible with memristor synapses, or used extra training circuitry thus eliminating much of the density advantages gained by using memristors, or were energy inefficient. Here we describe a novel CMOS spiking leaky integrate-and-fire neuron circuit. Building on a reconfigurable architecture with a single opamp, the described neuron accommodates a large number of memristor synapses, and enables online spike timing dependent plasticity (STDP) learning with optimized power consumption. Simulation results of an 180nm CMOS design showed 97% power efficiency metric when realizing STDP learning in 10,000 memristor synapses with a nominal 1M{\Omega} memristance, and only 13{\mu}A current consumption when integrating input spikes. Therefore, the described CMOS neuron contributes a generalized building block for large-scale brain-inspired neuromorphic systems.Comment: This is a preprint of an article accepted for publication in International Joint Conference on Neural Networks (IJCNN) 201

Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition

A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable circuit overhead. Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns. We present a versatile CMOS neuron that combines integrate-and-fire behavior, drives passive memristors and implements competitive learning in a compact circuit module, and enables in-situ plasticity in the memristor synapses. We demonstrate handwritten-digits recognition using the proposed architecture using transistor-level circuit simulations. As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive computing.Comment: This is a preprint of an article accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no. 2, June 201

A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive Synapses and In-Situ Learning

Nanoscale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs in-situ learning and computing while driving a large number of resistive synapses is desired. This work presents a novel leaky integrate-and-fire neuron design which implements the dual-mode operation of current integration and synaptic drive, with a single opamp and enables in-situ learning with crossbar resistive synapses. The proposed design was implemented in a 0.18 $\mu$m CMOS technology. Measurements show neuron's ability to drive a thousand resistive synapses, and demonstrate an in-situ associative learning. The neuron circuit occupies a small area of 0.01 mm$^2$ and has an energy-efficiency of 9.3 pJ$/$spike$/$synapse

Systematic Design of 10-Bit 50MS/s Pipelined ADC

A systematical design analysis of a 10-bit 50MS/s pipelined ADC is presented. With an opamp-sharing technique, the power consumption is reduced drastically. Simulated in a 130-nm CMOS process, it achieves a 58.9dB signal-to-noise ratio (SNR), a 9.3 effective number of bits (ENOB), 64dB spurious free dynamic range (SFDR) with a sinusoid input of 4.858-MHz 1-Vpp at 50MS/s, and consumes less than 24 mW from a 1.2-V supply

A 1 GS/s, 31 MHz BW, 76.3 dB Dynamic Range, 34 mW CT-ΔΣ ADC with 1.5 Cycle Quantizer Delay and Improved STF

A 1 GS/s continuous-time delta-sigma modulator (CT- ΔΣM) with 31 MHz bandwidth, 76.3 dB dynamic range and 72.5 dB peak-SNDR is reported in a 0.13 μm CMOS technology. The design employs an excess loop delay (ELD) of more than one clock cycle for achieving higher sampling rate. The ELD is compensated using a fast-loop formed around the last integrator by using a sample-and-hold. Further, the effect of this ELD compensation scheme on the signal transfer function (STF) of a feedforward CT- ΔΣ architecture has been analyzed and reported. In this work, an improved STF is achieved by using a combination of feed-forward, feed-back and feed-in paths and power consumption is reduced by eliminating the adder opamp. This CT- ΔΣ M has a conversion bandwidth of 31 MHz and consumes 34 mW from the 1.2 V power supply. The relevant design trade-offs have been investigated and presented along with simulation results

Compact Verilog-A Modeling of Silicon Traveling-Wave Modulator for Hybrid CMOS Photonic Circuit Design

A compact Verilog-A model of silicon-based junction traveling-wave Mach-Zehnder modulator (MZM) is developed for hybrid CMOS and photonic system-level simulation in Cadence environment. Critical device functions such as the voltage dependent change of refractive index, small-signal RLGC parameters for the MZM arms are extracted from the photonic device characterization from OpSIS foundry. Thermo-optical coefficient is also considered in the model. Simulation results including electro-optic S21 is characterized for the phase modulator\u27s bandwidth. Also, transient MZM operation with non-return to zero (NRZ) data transmission at 10 Gb/s and 20 Gb/s rates are demonstrated

A Comprehensive Design Approach for a MZM Based PAM-4 Silicon Photonic Transmitter

A 4-level pulse amplitude modulation (PAM-4) silicon photonic transmitter targeting operation at 25 Gb/s is designed using an electrical-photonic co-design methodology. The prototype consists of an electrical circuit and a photonics circuit, which were designed in 130 nm IBM SiGe BiCMOS process and 130nm IME SOI CMOS process, respectively. Then the two parts will be interfaced via side-by-side wire bonding. The electrical die mainly includes a 12.5 GHz PLL, a full-rate 4- channel uncorrelated 27 − 1 pseudo-random binary sequence (PRBS) generator and CML drivers. The photonics die is a 2-segment Mach-Zehnder modulator (MZM) silicon photonics device with thermal tuning feature for PAM-4. Verilog-A model for the MZM entails the system simulation for optical devices together with electrical circuitry using custom IC design tools. A full-rate 4-channel uncorrelated PRBS design using transition matrix method is detailed, in which any two of the 4-channels can be used for providing random binary sequence to drive the two segments of the MZM to generate the PAM-4 signal

A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive Synapses and \u3cem\u3eIn-Situ\u3c/em\u3e Learning

Nano-scale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs in-situ learning and computing while driving a large number of resistive synapses is desired. This work presents a novel leaky integrate-and-fire neuron design which implements the dual-mode operation of current integration and synaptic drive, with a single opamp and enables in-situ learning with crossbar resistive synapses. The proposed design was implemented in a 0.18μm CMOS technology. Measurements show neuron’s ability to drive a thousand resistive synapses, and demonstrate an in-situ associative learning. The neuron circuit occupies a small area of 0.01mm2 and has an energy-efficiency of 9.3pJ/spike/synapse