Jeffrey M. Shainline

Bio: Jeffrey M. Shainline is an academic researcher from National Institute of Standards and Technology. The author has contributed to research in topics: Photonics & Neuromorphic engineering. The author has an hindex of 27, co-authored 139 publications receiving 3025 citations. Previous affiliations of Jeffrey M. Shainline include Brown University & University of Colorado Boulder.

Single-chip microprocessor that communicates directly using light

Abstract: An electronic–photonic microprocessor chip manufactured using a conventional microelectronics foundry process is demonstrated; the chip contains 70 million transistors and 850 photonic components and directly uses light to communicate to other chips. The rapid transfer of data between chips in computer systems and data centres has become one of the bottlenecks in modern information processing. One way of increasing speeds is to use optical connections rather than electrical wires and the past decade has seen significant efforts to develop silicon-based nanophotonic approaches to integrate such links within silicon chips, but incompatibility between the manufacturing processes used in electronics and photonics has proved a hindrance. Now Chen Sun et al. describe a 'system on a chip' microprocessor that successfully integrates electronics and photonics yet is produced using standard microelectronic chip fabrication techniques. The resulting microprocessor combines 70 million transistors and 850 photonic components and can communicate optically with the outside world. This result promises a way forward for new fast, low-power computing systems architectures. Data transport across short electrical wires is limited by both bandwidth and power density, which creates a performance bottleneck for semiconductor microchips in modern computer systems—from mobile phones to large-scale data centres. These limitations can be overcome1,2,3 by using optical communications based on chip-scale electronic–photonic systems4,5,6,7 enabled by silicon-based nanophotonic devices8. However, combining electronics and photonics on the same chip has proved challenging, owing to microchip manufacturing conflicts between electronics and photonics. Consequently, current electronic–photonic chips9,10,11 are limited to niche manufacturing processes and include only a few optical devices alongside simple circuits. Here we report an electronic–photonic system on a single chip integrating over 70 million transistors and 850 photonic components that work together to provide logic, memory, and interconnect functions. This system is a realization of a microprocessor that uses on-chip photonic devices to directly communicate with other chips using light. To integrate electronics and photonics at the scale of a microprocessor chip, we adopt a ‘zero-change’ approach to the integration of photonics. Instead of developing a custom process to enable the fabrication of photonics12, which would complicate or eliminate the possibility of integration with state-of-the-art transistors at large scale and at high yield, we design optical devices using a standard microelectronics foundry process that is used for modern microprocessors13,14,15,16. This demonstration could represent the beginning of an era of chip-scale electronic–photonic systems with the potential to transform computing system architectures, enabling more powerful computers, from network infrastructure to data centres and supercomputers.

Open foundry platform for high-performance electronic-photonic integration

Abstract: This paper presents photonic devices with 3 dB/cm waveguide loss fabricated in an existing commercial electronic 45 nm SOI-CMOS foundry process. By utilizing existing front-end fabrication processes the photonic devices are monolithically integrated with electronics in the same physical device layer as transistors achieving 4 ps logic stage delay, without degradation in transistor performance. We demonstrate an 8-channel optical microring-resonator filter bank and optical modulators, both controlled by integrated digital circuits. By developing a device design methodology that requires zero process infrastructure changes, a widely available platform for high-performance photonic-electronic integrated circuits is enabled.

Superconducting Optoelectronic Circuits for Neuromorphic Computing

Abstract: To realize functionality similar to that of their biological inspirations, advanced neuromorphic systems require massive interconnectivity, extreme energy efficiency, and complex signaling mechanisms. The authors propose an integrated optoelectronic platform combining superconducting electronics with photonic signaling, to enable neuromorphic computing beyond the scale of the human brain.

A 45 nm CMOS-SOI Monolithic Photonics Platform With Bit-Statistics-Based Resonant Microring Thermal Tuning

Abstract: The microring resonator is critical for dense wavelength division multiplexed (DWDM) chip-to-chip optical I/O, enabling modulation and channel selection at the $\upmu\text{m}$ -scale suitable for a VLSI chip. Microring-based links, however, require active tuning to counteract process and thermo-optic variations. Here, we present a bit-statistical tuner that decouples tracking of optical one- and zero-levels to realize non-dc-balanced data transmission, an “eye-max”-locking controller, and self-heating cancellation without need for a high-speed sensing frontend. We implement the tuner on a 45 nm CMOS-SOI process with monolithically integrated photonic devices and circuits. The tuner consumes 0.74 mW in the logic while achieving a record 524 GHz ( $>$ 50 K temperature) tuning range at $3.8\;\upmu\text{W/GHz}$ heater efficiency. To our knowledge, this is the highest range and heater efficiency reported by an on-chip closed-loop thermal tuner to date. The tuner integrates with a 5 Gb/s 30 fJ/bit monolithic microring transmitter, achieving wavelength-lock and immunity to both tracking failures and self-heating events caused by arbitrary, non-dc-balanced bitstreams. In addition, the tuner provides critical functionality for an 11- $\lambda$ DWDM transmitter macro capable of $11\ \times\ 8$ Gb/s bandwidth on a fiber. Together with the transmitter, a 10 Gb/s on-chip monolithic optical receiver with $10^{-{\textbf{12}}}$ BER sensitivity of $9\;\upmu\text{A}$ at 10 Gb/s enables a sub-pJ/bit 5 Gb/s optical chip-to-chip link, with the bit-statistical tuner providing thermally robust microring operation.

Superconducting optoelectronic circuits for neuromorphic computing

Abstract: Neural networks have proven effective for solving many difficult computational problems. Implementing complex neural networks in software is very computationally expensive. To explore the limits of information processing, it will be necessary to implement new hardware platforms with large numbers of neurons, each with a large number of connections to other neurons. Here we propose a hybrid semiconductor-superconductor hardware platform for the implementation of neural networks and large-scale neuromorphic computing. The platform combines semiconducting few-photon light-emitting diodes with superconducting-nanowire single-photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes fanout and parasitic constraints on electrical signals while simultaneously introducing physical degrees of freedom which can be employed for computation. The use of supercurrents achieves the low power density necessary to scale to systems with enormous entropy. The proposed processing units can operate at speeds of at least $20$ MHz with fully asynchronous activity, light-speed-limited latency, and power densities on the order of 1 mW/cm$^2$ for neurons with 700 connections operating at full speed at 2 K. The processing units achieve an energy efficiency of $\approx 20$ aJ per synapse event. By leveraging multilayer photonics with deposited waveguides and superconductors with feature sizes $>$ 100 nm, this approach could scale to systems with massive interconnectivity and complexity for advanced computing as well as explorations of information processing capacity in systems with an enormous number of information-bearing microstates.

Deep learning with coherent nanophotonic circuits

Abstract: Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.

Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages

Abstract: Electro-optic modulators translate high-speed electronic signals into the optical domain and are critical components in modern telecommunication networks1,2 and microwave-photonic systems3,4. They are also expected to be building blocks for emerging applications such as quantum photonics5,6 and non-reciprocal optics7,8. All of these applications require chip-scale electro-optic modulators that operate at voltages compatible with complementary metal–oxide–semiconductor (CMOS) technology, have ultra-high electro-optic bandwidths and feature very low optical losses. Integrated modulator platforms based on materials such as silicon, indium phosphide or polymers have not yet been able to meet these requirements simultaneously because of the intrinsic limitations of the materials used. On the other hand, lithium niobate electro-optic modulators, the workhorse of the optoelectronic industry for decades9, have been challenging to integrate on-chip because of difficulties in microstructuring lithium niobate. The current generation of lithium niobate modulators are bulky, expensive, limited in bandwidth and require high drive voltages, and thus are unable to reach the full potential of the material. Here we overcome these limitations and demonstrate monolithically integrated lithium niobate electro-optic modulators that feature a CMOS-compatible drive voltage, support data rates up to 210 gigabits per second and show an on-chip optical loss of less than 0.5 decibels. We achieve this by engineering the microwave and photonic circuits to achieve high electro-optical efficiencies, ultra-low optical losses and group-velocity matching simultaneously. Our scalable modulator devices could provide cost-effective, low-power and ultra-high-speed solutions for next-generation optical communication networks and microwave photonic systems. Furthermore, our approach could lead to large-scale ultra-low-loss photonic circuits that are reconfigurable on a picosecond timescale, enabling a wide range of quantum and classical applications5,10,11 including feed-forward photonic quantum computation. Chip-scale lithium niobate electro-optic modulators that rapidly convert electrical to optical signals and use CMOS-compatible voltages could prove useful in optical communication networks, microwave photonic systems and photonic computation.

Single-chip microprocessor that communicates directly using light

Abstract: An electronic–photonic microprocessor chip manufactured using a conventional microelectronics foundry process is demonstrated; the chip contains 70 million transistors and 850 photonic components and directly uses light to communicate to other chips. The rapid transfer of data between chips in computer systems and data centres has become one of the bottlenecks in modern information processing. One way of increasing speeds is to use optical connections rather than electrical wires and the past decade has seen significant efforts to develop silicon-based nanophotonic approaches to integrate such links within silicon chips, but incompatibility between the manufacturing processes used in electronics and photonics has proved a hindrance. Now Chen Sun et al. describe a 'system on a chip' microprocessor that successfully integrates electronics and photonics yet is produced using standard microelectronic chip fabrication techniques. The resulting microprocessor combines 70 million transistors and 850 photonic components and can communicate optically with the outside world. This result promises a way forward for new fast, low-power computing systems architectures. Data transport across short electrical wires is limited by both bandwidth and power density, which creates a performance bottleneck for semiconductor microchips in modern computer systems—from mobile phones to large-scale data centres. These limitations can be overcome1,2,3 by using optical communications based on chip-scale electronic–photonic systems4,5,6,7 enabled by silicon-based nanophotonic devices8. However, combining electronics and photonics on the same chip has proved challenging, owing to microchip manufacturing conflicts between electronics and photonics. Consequently, current electronic–photonic chips9,10,11 are limited to niche manufacturing processes and include only a few optical devices alongside simple circuits. Here we report an electronic–photonic system on a single chip integrating over 70 million transistors and 850 photonic components that work together to provide logic, memory, and interconnect functions. This system is a realization of a microprocessor that uses on-chip photonic devices to directly communicate with other chips using light. To integrate electronics and photonics at the scale of a microprocessor chip, we adopt a ‘zero-change’ approach to the integration of photonics. Instead of developing a custom process to enable the fabrication of photonics12, which would complicate or eliminate the possibility of integration with state-of-the-art transistors at large scale and at high yield, we design optical devices using a standard microelectronics foundry process that is used for modern microprocessors13,14,15,16. This demonstration could represent the beginning of an era of chip-scale electronic–photonic systems with the potential to transform computing system architectures, enabling more powerful computers, from network infrastructure to data centres and supercomputers.

All-optical spiking neurosynaptic networks with self-learning capabilities.

Abstract: Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses. Such hardware, when connected in networks or neuromorphic systems, processes information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system, capable of supervised and unsupervised learning. We exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain. Such photonic neurosynaptic networks promise access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data. An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.

A Review of Scientific Instruments

Abstract: Journal of Scientific Instruments Editor: Dr H R Lang Vol 28 and Supplement No 1, 1951 Pp xvi + 388 + iii + 80 (London: Institute of Physics, 1951) Bound, £3 12s; unbound, £3