As big strides were being made in many science fields in the 1970s and 80s, faster computation for solving problems in molecular biology, semiconductor technology, aeronautics, particle physics, etc., was at the forefront of research. Parallel and super-computers were introduced, which enabled problems of a higher level of complexity to be solved. At about the same time, Nobel-laureate physicist Richard Feynman launched what seemed at the time a wild idea; to build a computer based on quantum physics concepts such as superposition and entanglement [1]. The outrageousness of his ideas is documented in the book 'Surely, You're Joking, Mr. Feynman' [2].

This brief deals with the impact of spin-transfer torque magnetic random access memory (STT-MRAM) cell based on double-barrier magnetic tunnel junction (DMTJ) on the performance of a two-layer multilayer perceptron (MLP) neural network. The DMTJ-based cell is benchmarked against the conventional single-barrier MTJ (SMTJ) counterpart by means of a comprehensive evaluation carried out through a state-of-the-art device-to-algorithm simulation framework. The benchmark is based on the MNIST handwritten dataset, Verilog-A MTJ compact models developed by our group, and 0.8 V FinFET technology. Our results point out that the use of DMTJ-based STT-MRAM cells to implement digital embedded non-volatile memory (eNVM) synaptic core allows write/read energy and latency improvements of about 53%/61% and 66%/17%, respectively, as compared to the SMTJ-based equivalent design. This is achieved by ensuring a reduced area footprint and a learning accuracy of about 91%. Such results make the DMTJ-based STT-MRAM cell a good eNVM option for neuro-inspired computing.

Quantum computers process information stored in quantum bits (qubits), which must be controlled and read out by a traditional electronic interface. Co-designing and cooptimizing such a quantum-classical complex system requires efficient simulators to emulate the qubits and their interaction with classical electronics. For spin-qubit readout, a single electron transistor (SET) is often employed. To build a toolset that can co-simulate the spin qubit system with the classical control and readout interface, a compact and efficient SET model is needed. This paper presents a new compact empirical SET model based on state-of-the-art SET measurement and extracted by a custom function-fitting python program. Within the target source-drain voltage range of ±1000μV , the model is accurate for circuit (SPICE) simulation. Furthermore, the empirical model is represented by a set of equations that enables instantaneous output response requiring a negligible simulation time. With this new SET model, a quantum-electronics co-simulator such as SPINE can now be enhanced to simulate the readout in addition to the control circuits of spin qubits, thus enabling the design of the complete integrated circuit (IC) required for large-scale quantum computers.

Over the past decade, significant progress in quantum technologies has been made, and hence, engineering of these systems has become an important research area. Many researchers have become interested in studying ways in which classical integrated circuits can be used to complement quantum mechanical systems, enabling more compact, performant, and/or extensible systems than would be otherwise feasible. In this article - written by a consortium of early contributors to the field - we provide a review of some of the early integrated circuits for the quantum information sciences. Complementary metal - oxide semiconductor (CMOS) and bipolar CMOS (BiCMOS) integrated circuits for nuclear magnetic resonance, nitrogen-vacancy-based magnetometry, trapped-ion-based quantum computing, superconductor-based quantum computing, and quantum-dot-based quantum computing are described. In each case, the basic technological requirements are presented before describing proof-of-concept integrated circuits. We conclude by summarizing some of the many open research areas in the quantum information sciences for CMOS designers.

This work presents an experimental study of different components (resistors, diodes, transistors) in a standard 40-nm bulk CMOS process for their suitability as integrated cryogenic temperature sensors down to a temperature of 4.2K. It was found that most devices can be employed as sensors down to temperatures of approximately 50K, below which non-ideal effects such as non-linear behaviour and decreased sensitivity start to dominate. The Dynamic-Threshold MOS (DTMOS) was found to be a very promising candidate for its linearity, low forward-voltage-drop and sensitivity down to 8K. Moreover, as previous research indicated that cryogenic self-heating raises the local chip temperature to tens of Kelvins already at moderate power levels, the aforementioned sensing limitations at very low temperatures are expected to be of less importance in realistic applications. The results presented in this work contribute to the further integration of classical cryo-CMOS control electronics and qubits, towards a fully scalable quantum computer.

This article describes the challenges and opportunities encountered in designing an electronic interface for quantum processors. It focuses on the use of standard CMOS technology to design and fabricate integrated circuits (ICs) operating at cryogenic temperatures. The article also focuses on spin qubits possibly operated in the high milli-Kelvin or even in the low Kelvin domain. To realize a spin qubit, a single electron is isolated in an extremely small site on the surface of a semiconductor die. A large magnetic field is applied to ensure that the spin-up and spin-down states of the electron correspond to distinct energy levels. Those two states are then used to encode the qubit quantum states.

This work presents a self-heating study of a 40-nm bulk-CMOS technology in the ambient temperature range from 300 K down to 4.2 K. A custom test chip was designed and fabricated for measuring both the temperature rise in the MOSFET channel and in the surrounding silicon substrate, using the gate resistance and silicon diodes as sensors, respectively. Since self-heating depends on factors such as device geometry and power density, the test structure characterized in this work was specifically designed to resemble actual devices used in cryogenic qubit control ICs. Severe self-heating was observed at deep-cryogenic ambient temperatures, resulting in a channel temperature rise exceeding 50 K and having an impact detectable at a distance of up to 30 μm from the device. By extracting the thermal resistance from measured data at different temperatures, it was shown that a simple model is able to accurately predict channel temperatures over the full ambient temperature range from deep-cryogenic to room temperature. The results and modeling presented in this work contribute towards the full self-heating-aware IC design-flow required for the reliable design and operation of cryo-CMOS circuits.

Cryogenic CMOS (cryo-CMOS) is a viable technology for the control interface of the large-scale quantum computers able to address non-trivial problems. In this paper, we demonstrate state-of-the-art cryo-CMOS circuits and systems for such application and we discuss the challenges still to be faced on the path towards practical quantum computers.

This paper presents a device matching study of a commercial 40-nm bulk CMOS technology operated at cryogenic temperatures. Transistor pairs and linear arrays, optimized for device matching, were characterized over the temperature range from 300 K down to 4.2 K. The device parameters relevant for mismatch, i.e., the threshold voltage and the current factor, were extracted, from which the change in both absolute value and variability as a function of temperature and device size were investigated. It is shown that the Pelgrom scaling law is valid also at 4.2 K and that the simplified Croon model is able to accurately predict drain-current mismatch from moderate to strong inversion over the entire temperature range. Additionally, the characterization of linear device arrays shows exacerbated edge-effects at extremely low temperatures, thus requiring the addition of dummy devices at the array boundary. The result of this study is the first model capable of predicting mismatch over a wide range of operating regions and temperatures.

Cryogenic device models are essential for the reliable design of the cryo-CMOS interface that enables large-scale quantum computers. In this paper, mismatch characterization and modeling of a 40-nm bulk CMOS process over the 4.2-300 K temperature range is studied, towards an all-operating-region mismatch model. An overall increase of variability is shown, in particular in the subthreshold region at cryogenic temperatures due to a dramatic increase of the subthreshold slope mismatch. Mismatch in strong inversion is modeled by the Croon model while the weak-inversion region is modeled by taking subthresh-old slope variability into account. This results in the first model capable of predicting mismatch over the whole range of operating regions and temperatures.

A quantum computer comprises a quantum processor and the associated control electronics used to manipulate the qubits at the core of a quantum processor. CMOS circuits placed close to the quantum bits and operating at cryogenic temperatures offer the best solution for the control of millions of qubits. The performance requirements of the electronics are very stringent and its design requires the simultaneous optimization of both the circuits and the quantum system. This paper presents the SPINE (SPIN Emulator) toolset for the co-design and co-optimization of electronic/quantum systems. It comprises a SPICE simulator enhanced with a Verilog-A model based on a Hamiltonian solver emulating the quantum behavior of single-electron spin qubits. A co-design methodology is proposed to derive on the one hand the specifications of the electrical signals to be applied to and captured from the qubits, and to ensure on the other hand, the compliance of the electronics in generating the required signals. This methodology results in an optimized qubit performance while considering practical trade-offs in the control circuits, such as power consumption, complexity and cost as proven by a practical design example.

Accurate and low-noise generation and amplification of microwave signals are required for the manipulation and readout of quantum bits (qubits). A fault-tolerant quantum computer operates at deep cryogenic temperatures (i.e., <100 mK) and requires thousands of qubits for running practical quantum algorithms. Consequently, CMOS radio-frequency (RF) integrated circuits operating at cryogenic temperatures down to 4 K (Cryo-CMOS) offer a higher level of system integration and scalability for future quantum computers. In this paper, we extensively discuss the role, benefits, and constraints of Cryo-CMOS for qubits control and readout. The main characteristics of the CMOS transistors and their impacts on RF circuit designs are described. Furthermore, opportunities and challenges of low noise RF signal generation and amplification are investigated.

Cryogenic characterization and modeling of two nanometer bulk CMOS technologies (0.16-&#x03BC;m and 40-nm) are presented in this paper. Several devices from both technologies were extensively characterized at temperatures of 4 K and below. Based on a detailed understanding of the device physics at deep-cryogenic temperatures, a compact model based on MOS11 and PSP was developed. In addition to reproducing the device DC characteristics, the accuracy and validity of the compact models are demonstrated by comparing time-and frequency-domain simulations of complex circuits, such as a ring oscillator and a low-noise amplifier (LNA), with the measurements at 4 K.

The design of cryogenic interface electronics enabling future scalable quantum computers requires the accurate characterization and modeling of nanometer CMOS processes at cryogenic temperatures. To this end, this paper presents the mismatch characterization of 40-nm bulk CMOS transistors over the temperature range from 300 K down to 4.2 K. Measured data confirm that variability increases at cryogenic temperatures, and analysis of such data proves the validity of both the Pelgrom and the Croon models, which describe the mismatch dependency on device area and bias conditions, respectively.

A quantum computer fundamentally comprises a quantum processor and a classical controller. The classical electronic controller is used to correct and manipulate the qubits, the core components of a quantum processor. To enable quantum computers scalable to millions of qubits, as required in practical applications, the simultaneous optimization of both the classical electronic and quantum systems is needed. In this paper, a co-design methodology is proposed for obtaining an optimized qubit performance while considering practical trade-offs in the control circuits, such as power consumption, complexity, and cost. The SPINE (SPIN Emulator) toolset is introduced for the co-design and co-optimization of electronic/quantum systems. It comprises a circuit simulator enhanced with a Verilog-A model emulating the quantum behavior of single-electron spin qubits. Design examples show the effectiveness of the proposed methodology in the optimization, design and verification of a whole electronic/quantum system.

The characterization of nanometer CMOS transistors of different aspect ratios at deep-cryogenic temperatures (4 K and 100 mK) is presented for two standard CMOS technologies (40 nm and 160 nm). A detailed understanding of the device physics at those temperatures was developed and captured in an augmented MOS11/PSP model. The accuracy of the proposed model is demonstrated by matching simulations and measurements for DC and time-domain at 4 K and, for the first time, at 100 mK.

Quantum computers1 could revolutionize computing in a profound way due to the massive speedup they promise. A quantum computer comprises a cryogenic quantum processor and a classical electronic controller. When scaling up the cryogenic quantum processor to at least a few thousands, and possibly millions, of qubits required for any practical quantum algorithm, cryogenic CMOS (cryo-CMOS) electronics is required to allow feasible and compact interconnections between the controller and the quantum processor. Cryo-CMOS leverages the CMOS fabrication infrastructure while exploiting the continuous improvement of performance and miniaturization guaranteed by Moore's law, in order to enable the fabrication of a cost-effective practical quantum computer. However, designing cryo-CMOS integrated circuits requires a new set of CMOS device models, their embedding in design and verification tools, and the possibility to co-simulate the cryo-CMOS/quantum-processor architecture for full-system optimization. In this paper, we address these challenges by focusing on their impact on the design of complex cryo-CMOS systems.

Quantum computers could efficiently solve problems that are intractable by today's computers, thus offering the possibility to radically change entire industries and revolutionize our lives. A quantum computer comprises a quantum processor operating at cryogenic temperature and an electronic interface for its control, which is currently implemented at room temperature for the few qubits available today. However, this approach becomes impractical as the number of qubits grows towards the tens of thousands required for complex quantum algorithms with practical applications. We propose an electronic interface for sensing and controlling qubits operating at cryogenic temperature implemented in standard CMOS.