Well-understood materials will likely play a key role in the quantum era.
With the advent of quantum computing, the need for peripheral fault-tolerant logic control circuitry has reached new heights. In classical computation, the unit of information is a “1” or “0”. In quantum computers, the unit of information is a qubit which can be characterized as a “0”, “1”, or a superposition of both values (known as a “superimposed state”).
The control circuitry in classical computers is CMOS (semiconductor) based, due to its high-performance and low power dissipation. The “1’s” and “0’s” of a classical computer can be manipulated, stored, and easily read using CMOS chips that operate at room temperature. Most quantum computers today operate at cryogenic temperatures, to ensure that the qubit remains coherent (in a superimposed state) for as long as possible. The coherence times are typically very short (nanoseconds to milliseconds) in a quantum computer, prompting the need for control circuitry that can perform high-speed, fault-tolerant operations. This requirement could be met by conventional CMOS control circuitry if it could be operated at cryogenic temperatures.
The first attempt to characterize semiconductor materials at cryogenic temperatures was made by A.K. Jonscher in his 1964 Proceedings of the IEEE publication, entitled “Semiconductors at Cryogenic Temperatures” [1]. His two basic conclusions were: 1) semiconductor devices have no major cryogenic application at that point in time due to “no real technological justification for going on a large scale to these extreme temperatures”, and 2) “the properties of semiconductor materials at cryogenic temperatures are so strikingly different from the familiar properties at higher temperatures, that it is reasonable to expect many more device applications to emerge as a result of continued research and development effort in this direction”. A few years later, IBM became interested in low-temperature semiconductor device operation [2-3] and concluded that MOSFET semiconductor devices show improved performance at cryogenic temperatures. With the advantages of low-temperature operation, scaling-down the cooling apparatus is still an obstacle in using semiconductor-based control circuitry.
Enter quantum mechanics. In 1959, Richard Feynman challenged the scientific community to employ quantum mechanics in the design of information processing systems. He envisioned new information systems and functions that involved quantized energy levels, and/or the interactions of quantized “spins” (angular momentum of quantum particles). His vision was realized in the 1980s, when it was demonstrated that quantum mechanical, energy-based equations could represent a universal Turing (computational) machine [4]. In 1994, it was shown that a quantum computer could factor integer numbers much more quickly than a classical computer (“in polynomial time”) [5]. This discovery was the catalyst that fostered continued interest in building quantum computing systems. That interest continues today at numerous commercial, research and academic organizations.
Even with the strong interest in building quantum computers, the fact remains that successful operation of this type of computer currently requires a cryogenic temperature environment. Quantum logic control circuitry will also need to operate at these cryogenic temperatures to function effectively in this environment. Thus, we have seen a resurgence of interest in the cryogenic temperature performance of CMOS-based circuitry.
Quantum computers do not require state-of-the-art CMOS circuitry, but CMOS devices operate differently at cryogenic and room temperatures. CMOS transistor performance (and the associated I-V performance) has recently been measured on 40 nm and 160 nm bulk CMOS devices, at both room temperature and at 4.2 degrees Kelvin (see Figure 1). Drive current increases at cryogenic temperatures due to an increase of the mobility in silicon at these temperature. Unfortunately, other effects such as substrate freeze-out can limit the increase in drive current at these low temperatures.
![Fig. 1. Measured I-V characteristics of nMOS transistors fabricated in 160nm (left) and 40nm (right) CMOS. Room temperature operation is shown in the dotted curves, liquid helium operation is shown in the solid curves, and a Spice-compatible model fitted to experimental data is shown in the dashed lines. (Taken from [6])](https://doxamagazine.org/wp-content/uploads/2022/06/Fig.-1.-Measured-I-V-characteristics-of-nMOS-transistors-fabricated-in-160nm-left-and-40nm-right-CMOS.-600x220.webp)
![Fig. 2. Silicon spin qubit centered in the dotted circle, control and readout signals (M, P, R, T, and Q) are shown in the inset. Simplified schematics of the quantum point contact and corollary circuits are shown. The voltage source is implemented as a digital-to-analog converter at room temperature. (Taken from [6])](https://doxamagazine.org/wp-content/uploads/2022/06/Fig.-2.-Silicon-spin-qubit-centered-in-the-dotted-circle-control-and-readout-600x297.webp)
In more futuristic applications, optical communications to and from the qubit may also be necessary. In this case, integrated CMOS circuits will also need to include micro- and nano-optical structures, such as light-guides and interferometers. These types of optical functions have been successfully demonstrated on room-temperature CMOS devices. Demonstrating this level of optical communications functionality at cryogenic temperatures may also be desirable in future quantum computing applications.
References
1.) A. K. Jonscher, “Semiconductors at Cryogenic Temperatures”, Proceedings of the IEEE, 1964.
2.) R. W. Keyes, et al., “The Role of Low Temperatures in the Operation of Logic Circuitry,” Proc. IEEE, vol. 58, pp. 1914-1932, 1970.
3.) F. H. Gaensslen, et al., “Very Small MOSFET’s for Low Temperature operation,” IEEE Trans. Electron Devices, vol. ED-24, pp. 218-229, 1877.
4.) P. Benioff, “The Computer as a Physical System: A Microscopic Quantum Mechanical Hamiltonian Model of Computers as Represented by Turing Machines,” J. Stat. Phys., vol. 22, no. 5, pp. 563-591, 1980.
5.) P. Shor, “Algorithms for Quantum Computations: Discrete Log and Factoring,” Proc. 35th Annu. Symp. Found. Comput. Sci., Los Alamitos, CA, 1994, pp. 124-134.
6.) E. Charbon, et al., “Cryo-CMOS for Quantum Computing,” 2016 IEDM, pp. 343-346.
