Cryogenic electronics plays a fundamental role in several applications, such as spacecraft, high-energy physics experiments, metrology, superconductive astronomical detectors and, with the increased interest in quantum computing, the manipulation of quantum bits (qubits) [1]. Outstanding characteristics have been reported for advanced CMOS technologies operating at cryogenic temperature in terms of on-state current, leakage current, subthreshold swing, and transconductance [2]. This represents an excellent opportunity to use such advanced technologies to design and implement a quantum computing control system (including multiplexers, LNAs, and RF oscillators) and introduce it inside the refrigerator together with the qubits [3]. Another recently growing field of application is the cryogenic silicon photonics [4].<\/p>\n
However, finding the optimal trade-off in the design of semiconductor devices, including p-n and p-i-n junction based devices, MOSFETs, and CMOS circuits is challenging due to the lack of efficient physics-based models valid down to cryogenic and deep-cryogenic temperatures – typically 4.2 K (liquid helium) or even below for quantum computing applications. Physics-based analysis and design of devices operating below 77 K have been held up by the numerical difficulties of modeling and simulation in this temperature range [5, 6]. The intrinsic carrier concentration becomes extremely small at cryogenic temperatures, which has its inevitable root in the exponential temperature scaling of the Fermi-Dirac and Boltzmann statistics.<\/p>\n
TCAD simulations of devices operating at such low temperatures, especially below 50 K, have always posed a challenge. Below 50 K, carrier and ionization statistics develop sharp transitions which cause slower convergence. As the temperature (T) decreases, the intrinsic carrier concentration (ni<\/sub><\/em>) dramatically decreases. For example, the silicon ni which at T=300K is around 1.5e+10 cm-3<\/sup>, at T=10K becomes 1.2e-267 cm-3<\/sup>, and at T=4.2K\u00a0ni<\/sub><\/em>= 2.7e-678 cm-3<\/sup>\u00a0[3], lying outside the range of IEEE double precision arithmetic (1e-308 \u2013 1e+308) starting from below T=8K. In wider-bandgap materials, such as GaAs, SiC, or GaN, the\u00a0ni<\/sub><\/em>\u00a0values become even lower. Therefore, in quasi-neutral or depletion regions, the minority carrier concentration can easily underflow. This is computationally very demanding in numerical TCAD simulations [5]. Such situation requires using a significantly higher floating point precision, as well as special numerical techniques, which are available in Silvaco\u2019s TCAD device simulation tools.<\/p>\n Cryogenic electronics plays a fundamental role in several applications, such as spacecraft, high-energy physics experiments, metrology, superconductive astronomical detectors and, with the increased interest in quantum computing, the manipulation of quantum bits (qubits) [1]. Outstanding characteristics have been reported for advanced CMOS technologies operating at cryogenic temperature in terms of on-state current, leakage current, subthreshold swing, and transconductance [2]. This represents an excellent opportunity to use such advanced technologies to design and implement a quantum computing control system (including multiplexers, LNAs, and RF oscillators) and introduce it inside the refrigerator together with the qubits [3]. Another recently growing field of application is the cryogenic silicon photonics [4].<\/p>\n","protected":false},"author":5,"featured_media":19272,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7486],"tags":[],"class_list":["post-30716","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-simulation-standard-ko","entry","has-media"],"_links":{"self":[{"href":"https:\/\/silvaco-stage.betagentechnologies.com\/ko\/wp-json\/wp\/v2\/posts\/30716","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/silvaco-stage.betagentechnologies.com\/ko\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/silvaco-stage.betagentechnologies.com\/ko\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/silvaco-stage.betagentechnologies.com\/ko\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/silvaco-stage.betagentechnologies.com\/ko\/wp-json\/wp\/v2\/comments?post=30716"}],"version-history":[{"count":1,"href":"https:\/\/silvaco-stage.betagentechnologies.com\/ko\/wp-json\/wp\/v2\/posts\/30716\/revisions"}],"predecessor-version":[{"id":30721,"href":"https:\/\/silvaco-stage.betagentechnologies.com\/ko\/wp-json\/wp\/v2\/posts\/30716\/revisions\/30721"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/silvaco-stage.betagentechnologies.com\/ko\/wp-json\/wp\/v2\/media\/19272"}],"wp:attachment":[{"href":"https:\/\/silvaco-stage.betagentechnologies.com\/ko\/wp-json\/wp\/v2\/media?parent=30716"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/silvaco-stage.betagentechnologies.com\/ko\/wp-json\/wp\/v2\/categories?post=30716"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/silvaco-stage.betagentechnologies.com\/ko\/wp-json\/wp\/v2\/tags?post=30716"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/silvaco-stage.betagentechnologies.com\/wp-content\/uploads\/2020\/02\/simstd_Q2_a1_2018.jpg\")
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