Bibliographic record
Abstract
Advancements in semiconductor technology have led to a steady increase in the unity power gain frequency ( f max ) of silicon transistors, both in CMOS and SiGe BiCMOS technologies. This, in turn, enables realization of complex monolithic silicon integrated circuits operating at the millimeter-wave (mm-wave) frequency range (typically defined as 30–300 GHz). Prime target applications of silicon mm-wave integrated systems include high-speed wireless access, satellite communications, high-resolution automotive radars, and imagers for security, industrial control, healthcare, and other applications. However, scaling of silicon transistors for high f max comes at the expense of reduced breakdown voltages, and hence limitations on output voltage swing and power. The link range and energy consumption of wireless systems are direct functions of the transmitter output power and efficiency, respectively. Efficient generation and amplification of radio-frequency (RF) modulated waveforms using silicon transistors is an ongoing challenge due to the reduced breakdown voltage of scaled silicon transistors, loss of passive components, and the conventional linearity–efficiency trade-off. This book covers the fundamentals, technology options, circuit architectures, and practical demonstrations of mm-wave wireless transmitters realized in silicon technologies. Why mm-waves? The main motivation to operate the wireless systems at higher carrier frequencies is the larger available bandwidth which translates to higher data rate in communication systems and higher resolution in ranging and imaging systems. Furthermore, the size of the antenna and circuitry, typically proportional to the wavelength, reduces with increasing carrier frequency. On the other hand, operating at higher frequencies poses two fundamental challenges. First, the loss of most materials increases with the frequency; therefore, compared with radio and microwave frequencies (below 30 GHz) the electromagnetic wave at mm-wave frequencies is attenuated more as it propagates in an environment (Fig. 1.1). It should be noted that over the mm-wave spectrum there are “windows” of relatively lower attenuation around 35 GHz, 90 GHz, 140 GHz, etc., and, consequently, these bands are often selected for mm-wave applications; on the other hand, high atmospheric attenuation levels around frequencies such as 60 GHz enable more aggressive frequency reuse, and are therefore often selected for small cell or secure communications applications. Second, the performance of semiconductor devices worsens with frequency; this includes reduced gain, increased noise, and more nonlinearity at higher frequencies for a given technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.452 | 0.332 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".