IEEE Spectrum

80 Pages·2022·19.705 MB·English

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VOLUME 59 / ISSUE 12 DECEMBER 2022
A New Way to 40
Squash Bugs
Functional programming can make
complex software less fragile.
By Charles Scalfani
Are You Ready for 46
Workplace Brain
Scanning?
The tech can boost productivity,
but will it make us happier?
By Evan Ackerman
& Eliza Strickland
EDITOR’S NOTE 2
NEWS 6
Scientific Machine Learning
5G and Aircraft
Meet the Posits
Sulfuric Acid Test
Geothermal Energy Storage
CAREERS 16
Inflation Hits U.S.
Engineering Salaries
HANDS ON 18
IEEE Spectrum’s Top
DIY Projects
NUMBERS DON’T LIE 21
Batteries Still Fall
The 22 The State of 30 Short of Liquid Fuels
the Transistor
NTO Transistor PAST FORWARD 76
PI In 75 years, it’s become The Music of the Transistor

IZ tiny, mighty, ubiquitous,
: L at 75 and just plain weird.
TOM By Samuel K. Moore THE INSTITUTE 69
T
BO & David Schneider Keeping Shelves

N; The First Transistor 24 Stocked
O
TI and How It Worked Taking Moore’s Law 32
C AI platform spots low inventory.
E
LL The point-contact transistor to New Heights
O
E C revolutionized electronics even When transistors can’t
OPP as its inventors struggled to get any smaller, the only
H understand it.
N direction is up.
A By Glenn Zorpette
H
T By Marko Radosavljevic &
A
N
O Jack Kavalieros
/J The Ultimate 29
M
EU Transistor Timeline
US The Transistor 38
M The transistor has been
R of 2047
TO reinvented over and over again.
S
SI By Stephen Cass Experts predict what
N
RA transistors will be like on
T
: ON THE COVER: their 100th birthday.
P
TO Illustration by Lisa Sheehan By Samuel K. Moore
DECEMBER 2022  SPECTRUM.IEEE.ORG  1
EDITOR’S NOTE BY HARRY GOLDSTEIN
The Device
That Changed
Everything
This replica of the original point-contact
transistor is on display outside IEEE Spectrum’s
Transistors are civilization’s conference rooms.
invisible infrastructure
Of course, this wouldn’t be a Spectrum special
issue if we didn’t tell you how the original
point-contact transistor worked, something that
I was roaming around the IEEE Spectrum office The best even the inventors seemed a little fuzzy on.
a couple of months ago, looking at the display explanation According to our editorial director for content
cases the IEEE History Center has installed in development, Glenn Zorpette, the best explana-
of the point-
the corridor that runs along the conference tion of the point-contact transistor is in Bardeen’s
contact
rooms at 3 Park. They feature photos of illustrious 1956 Nobel Prize lecture, but even that left out
transistor is
engineers, plaques for IEEE milestones, and a important details, which Zorpette explores in clas-
in Bardeen’s
handful of vintage electronics and memorabilia sic Spectrum style in “The First Transistor and
including an original Sony Walkman, an Edison 1956 Nobel How It Worked,” on page 24.
Mazda lightbulb, and an RCA Radiotron vacuum Prize lecture, And while we’re celebrating this historic
tube. And, to my utter surprise and delight, a but even accomplishment, Senior Editor Samuel K. Moore,
replica of the first point-contact transistor that left out who covers semiconductors for Spectrum and
invented by John Bardeen, Walter Brattain, and important curated this special issue, looks at what the tran-
William Shockley 75 years ago this month. sistor might be like when it turns 100. For “The
details.
I dashed over to our photography director, Transistor of 2047” [p. 38], Moore talked to the
Randi Klett, and startled her with my excitement, leading lights of semiconductor engineering, many
which, when she saw my discovery, she under- of them IEEE Fellows, to get a glimpse of a future
stood: We needed a picture of that replica, which where transistors are stacked on top of each other
she expertly shot and now accompanies this and are made of increasingly exotic 2D materials,
column. even as the OG of transistor materials, germanium,
What amazed me most besides the fact that is poised for a comeback.
the very thing this issue is devoted to was here When I was talking to Moore a few weeks ago
T
with us? I’d passed by it countless times and about this issue, he mentioned that he’s attending ET
L
never noticed it, even though it is tens of billions his favorite conference just as this issue comes K
I
D
times the size of one of today’s transistors. In fact, out, the 68th edition of IEEE’s International Elec- N
A
R
each of us is surrounded by billions, if not trillions tron Devices Meeting, in San Francisco. The ;
C
of transistors, none of which are visible to the mind-bending advances that emerge from that IA
B
naked eye. It is a testament to imagination and conference always get him excited about the engi- AL

ingenuity of three generations of electronics engi- neering feats occurring in today’s labs and on IO
G
neers who took the (by today’s standards) mam- tomorrow’s production lines. This year he’s most ER
S
moth point-contact transistor and shrunk it excited about new devices that combine comput- Y
B

down to the point where transistors are so ubiq- ing capability with memory to speed machine T
I
A
uitous that civilization as we know it would not learning. Who knows, maybe the transistor of 2047 R
T
R
exist without them. will make its debut there, too. O
P
2  SPECTRUM.IEEE.ORG  DECEMBER 2022
IEEE Print Ad.pdf 1 11/7/22 1:52 PM
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CONTRIBUTORS
 DAN GARISTO
Garisto is a freelance science
journalist who reports in this issue
on the latest machine-learning ACTING EDITOR IN CHIEF Harry Goldstein, [email protected] IEEE BOARD OF DIRECTORS
techniques in particle physics and EXECUTIVE EDITOR Jean Kumagai, [email protected] P+1R E73S2I D5E6N2T 3&92C8E O F Kax.J:. +R1a 7y3 L2iu 9, 8p1r [email protected]
math [p. 6]. In the process, he MANAGING EDITOR Elizabeth A. Bretz, [email protected] PRESIDENT-ELECT Saifur Rahman
effectively wound up reporting CREATIVE DIRECTOR TREASURER Mary Ellen Randall
Mark Montgomery, [email protected] SECRETARY John W. Walz
on how people working on these
PRODUCT MANAGER, DIGITAL Erico Guizzo, [email protected] PAST PRESIDENT Susan K. “Kathy” Land
problems “think about how
EDITORIAL DIRECTOR, CONTENT DEVELOPMENT
machines think,” he says. Glenn Zorpette, [email protected] VICE PRESIDENTS
Stephen M. Phillips, Educational Activities; Lawrence O. Hall,
SENIOR EDITORS Publication Services & Products; David A. Koehler, Member &
 ALLISON MARSH EStveapnh Aecnk Ceramssa (nS (pDeicgiiatal Pl),r oajcekcetrsm), [email protected]@eeieee.oer.gorg GJaemoegsra Ep. hMica Attchteivwitsi,e Ps;r eBsriudneon tM, Setyaenr,d Taercdhsn Aicsaslo Acciattivioitnie; s ;
Marsh, a historian of technology at STeakmlau Sel.  KP.e Mrryo,o tr.pe,e [email protected]@rgie ee.org Deborah M. Cooper, President, IEEE-USA
the University of South Carolina, Philip E. Ross, [email protected] DIVISION DIRECTORS
writes this month about a David Schneider, [email protected] Franco Maloberti (I); Ruth A. Dyer (II); Khaled Ben Letaief (III);
transistorized music box owned by Eliza Strickland, [email protected] Manfred “Fred” J. Schindler (IV); Cecilia Metra (V); Paul M.
John Bardeen [p. 76]. By the 1990s, ART & PRODUCTION Cunningham (VI); Claudio Cañizares (VII); Christina M. Schober
DEPUTY ART DIRECTOR Brandon Palacio, [email protected] (VIII); Ali H. Sayed (IX); Dalma Novak (X)
the box had succumbed to “inherent PHOTOGRAPHY DIRECTOR Randi Klett, [email protected]
vice,” a museum term for the ONLINE ART DIRECTOR Erik Vrielink, [email protected] REGION DIRECTORS
degradation of materials over time. PRINT PRODUCTION SPECIALIST Greg T. Gdowski (1); Barry C. Tilton (2); Theresa A. Brunasso (3);
Sylvana Meneses, [email protected] Johnson A. Asumadu (4); Bob G. Becnel (5);  Timothy T. Lee (6);
“‘Inherent vice’ is one of my favorite MULTIMEDIA PRODUCTION SPECIALIST Robert L. Anderson (7); Antonio Luque (8); Enrique A. Tejera (9);
phrases,” Marsh says. “It conjures up Michael Spector, [email protected] Deepak Mathur (10)
images of objects behaving badly.” WEB PRODUCTION Michael Novakovic, [email protected]
NEWS MANAGER Margo Anderson, [email protected] DIRECTOR EMERITUS Theodore W. Hissey
ASSOCIATE EDITORS IEEE STAFF
 MARKO RADOSAVLJEVIC Willie D. Jones (Digital), [email protected] EXECUTIVE DIRECTOR & COO Stephen Welby
Radosavljevic is a principal engineer Michael Koziol, [email protected] [phone], [email protected]
SENIOR COPY EDITOR Joseph N. Levine, [email protected] CHIEF INFORMATION OFFICER Cherif Amirat
in the components research group COPY EDITOR Michele Kogon, [email protected] [phone], [email protected]
at Intel. He and Jack Kavalieros, a EDITORIAL RESEARCHER Alan Gardner, [email protected] CHIEF MARKETING OFFICER Karen L. Hawkins
Fellow and vice president of device EDITORIAL INTERN Dina Genkina, [email protected] [phone], [email protected]
integration in the group, describe CONTRACT SPECIALIST Ramona L. Foster, [email protected] PUBLICATIONS Steven Heffner
a likely next step in the evolution of CCOhNarTleRsI BQU. TCIhNoGi, PEeDteIrT FOaRirSle yR,o Ebdedrt G Ne. nCth, aWre. Wttea,y Stt Gevibebns C , hMearrryk,  Harris, +C1O 2R1P2O 7R0A5T E8 9A5C8T, sI.VheIffTIneErS@ Dieoenen.oar gHourican
Moore’s Law on page 32. “We are Allison Marsh, Prachi Patel, Julianne Pepitone, Lawrence Ulrich, [phone], [email protected]
very optimistic that the industry will Emily Waltz MEMBER & GEOGRAPHIC ACTIVITIES Cecelia Jankowski
[phone], [email protected]
continue to find ways in which to THE INSTITUTE STANDARDS ACTIVITIES Konstantinos Karachalios
increase density in logic,” he says. EDITOR IN CHIEF Kathy Pretz, [email protected] [phone], [email protected]
ASSISTANT EDITOR Joanna Goodrich, [email protected] EDUCATIONAL ACTIVITIES Jamie Moesch
DIRECTOR, PERIODICALS PRODUCTION SERVICES Peter Tuohy [phone], [email protected]
 CHARLES SCALFANI ADVERTISING PRODUCTION MANAGER GENERAL COUNSEL & CHIEF COMPLIANCE OFFICER
Felicia Spagnoli, [email protected] Sophia A. Muirhead [phone], [email protected]
Scalfani is the chief technology SENIOR ADVERTISING PRODUCTION COORDINATOR CHIEF FINANCIAL OFFICER Thomas R. Siegert
officer of Panoramic Software, Nicole Evans Gyimah, [email protected] [phone], [email protected]
ADVERTISING PRODUCTION [phone] TECHNICAL ACTIVITIES Mary Ward-Callan
based in Dana Point, Calif. He has [phone], [email protected]
EDITOR IN CHIEF EMERITUS Susan Hassler, [email protected]
experienced firsthand the tendency ACTING MANAGING DIRECTOR, IEEE-USA Russell T. Harrison
of software systems to become ESDusIaTnO HRIaAssLl erA, DCVhIaSirO; REYll a BMO.A ARtDk,in sI, EREoEb erStP NE.C CThRaUrMette, Francis J. [phone], [email protected]
increasingly complex and fragile. “It Doyle III, Matthew Eisler, Shahin Farshchi, Alissa Fitzgerald, Jonathan IEEE PUBLICATION SERVICES & PRODUCTS BOARD
just gets out of control,” he says. Garibaldi, Benjamin Gross, Lawrence O. Hall, Jason K. Hui, Leah H. Lawrence O. Hall, Chair; Stefano Galli, Nazanin Bassiri Gharb,
“It’s like running downhill.” In an effort Jamieson, Mary Lou Jepsen, M ichel M. Maharbiz, Somdeb Majumdar, James Irvine, Clem Karl, Hulya Kirkici, Yong Lian, Fabrizio Lombardi,
Lisa May, Carmen S. Menoni, Ramune Nagisetty, Paul Nielsen, Sofia Peter Luh, Anna Scaglione, Gaurav Sharma, Isabel Trancoso,
to manage that complexity better, Olhede, Christopher Stiller, Wen Tong, Boon-Lock Yeo Peter Winzer, Bin Zhao, Weihua Zhuang
Scalfani was drawn to a paradigm EDITORIAL ADVISORY BOARD, THE INSTITUTE
called functional programming, which Kathy Pretz, Chair; Qusi Alqarqaz, Stamatis Dragoumanos, IEEE OPERATIONS CENTER
he describes on page 40. Jonathan Garibaldi, Madeleine Glick, Lawrence O. Hall, 445 Hoes Lane, Box 1331
Susan Hassler, Francesca Iacopi, Cecilia Metra, Shashi Raj Pandey, Piscataway, NJ 08854-1331 U.S.A.
John Purvis, Chenyang Xu Tel: [phone] Fax: [phone]
 LISA SHEEHAN MANAGING DIRECTOR, PUBLICATIONS Steven Heffner
DIRECTOR, BUSINESS DEVELOPMENT,
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THE LATEST DEVELOPMENTS IN TECHNOLOGY, ENGINEERING, AND SCIENCE DECEMBER 2022
ARTIFICIAL INTELLIGENCE Math and physics are old friends. Over
time, they’ve witnessed the rise and fall
Machine Learning of technological advances, including
slide rules, calculators, and aids like
Wolfram Alpha. Now, as the latest developments in
Rethinks Scientific
machine learning (ML) are being applied to prob-
lems in math and physics, these advances are raising
Thinking Neural nets
fundamental questions about what it means to
teach algorithms to think like us.
find new approaches to
X Plus Why?
physics and math “When we say computers are very good at math,
they’re very good at things that are quite specific,”
says Guy Gur-Ari, a machine-learning expert at
Google Research. Computers are good at arithme-
tic—plugging numbers in and calculating is com-
paratively straightforward stuff. But outside of
BY DAN GARISTO formal structures, computers struggle.
6  SPECTRUM.IEEE.ORG  DECEMBER 2022 Illustration by Chad Hagen
THE LATEST DEVELOPMENTS IN TECHNOLOGY, ENGINEERING, AND SCIENCE DECEMBER 2022
“ We’d like to have a machine learn
Solving mathematical word prob-
lems, or “quantitative reasoning,” is
to think more like a physicist. We
deceptively tricky because it requires a
robustness and rigor that many other also just need to learn how to think
problems don’t. Although ML models
a little bit more like a machine.”
trained on more data make fewer mis-
takes, scaling up only goes so far with
quantitative reasoning. Researchers have —JESSE THALER, MIT
begun to realize that the mistakes ML
language-generation models make in
solving word problems require a more
targeted approach. learning expert at OpenAI. Basart agrees. a human, the actual process they’re fol-
Last year, two different teams, at “That’s shocking. I thought it would take lowing could be wildly different. On the
the University of California, Berkeley, longer,” he says. other hand, “chain of thought prompt-
and at OpenAI, released two data sets, Minerva uses Google’s own Pathways ing” is familiar to any human student
MATH and GSM8K, respectively, which Language Model (PaLM), fine-tuned on who’s been asked to “show your work.”
contain thousands of math problems scientific papers from arXiv and other “I think there’s this notion humans
across geometry, algebra, precalculus, sources with formatted math. Two other doing math have some rigid reasoning
and more. “We basically wanted to see strategies helped Minerva. In “chain system that there’s a sharp distinction
if it was a problem with data sets,” says of thought prompting,” Minerva was between knowing something and not
Steven Basart, a researcher at the Center required to break down larger problems knowing something,” says Ethan Dyer,
for AI Safety who worked on MATH. into more palatable chunks. The model a machine-learning expert at Google
Could machine learning’s quantitative also used majority voting—instead of Research. But humans give inconsistent
reasoning errors be fixed by training on being asked for one answer, it was asked answers, make errors, and fail to apply
better formatted, bigger data sets? The to solve the problem 100 times. Of core concepts too. The borders, at this
MATH group found just how challenging those results, Minerva picked the most frontier of machine learning, are blurred.
quantitative reasoning is for top-of-the- common answer.
line ML language models: They scored The gains from these new strategies Think Like a Physicist
less than 7 percent. (A human grad stu- were enormous. Minerva shot up to 50 Particle physics data is unusual. While
dent scored 40 percent, while an Inter- percent accuracy on MATH and nearly convolutional neural nets (CNNs) have
national Mathematical Olympiad champ 80 percent accuracy on GSM8K, as well proven extremely effective at classifying
scored 90 percent.) as the MMLU, a more general set of images of everyday objects like trees,
Models attacking GSM8K ques- STEM questions that includes chemistry cats, and food, they’re not great for par-
tions, which had easier grade-school- and biology. When Minerva was asked to ticle collisions. The problem, according
level problems, reached about 20 redo a random sample of slightly tweaked to Javier Duarte, a particle physicist at
percent accuracy. To do even that well, questions, it performed just as well, sug- the University of California, San Diego,
OpenAI researchers used two tech- gesting that its capabilities were not from is that collision data such as that from
niques: fine-tuning and verification. mere memorization. the Large Hadron Collider, doesn’t nat-
In fine-tuning, researchers take a pre- What Minerva knows—or doesn’t urally work as an image.
trained language model that includes know—about math is fuzzier. Unlike Flashy depictions of collisions at the
irrelevant information and then show “proof assistants” used by mathemati- LHC can misleadingly fill up the entire
the model only the relevant information cians, which come with built-in struc- detector. In reality, only a few out of mil-
(math problems). Verification allows the ture, Minerva and other language models lions of inputs are registering a signal,
models to review their mistakes. have no formal structure. They can have like a white screen with a few black
At the time, OpenAI predicted a model strange, messy reasoning and still arrive pixels. This makes for a poor image in a
would need to be trained on 100 times at the right answer. As numbers grow CNN, but it can work well in a different,
as much data to reach 80 percent accu- larger, the language models’ accuracy fal- newer framework called graph neural
racy on GSM8K. But in June, Google’s ters, something that would never happen networks (GNNs).
Minerva announced 78 percent accu- on a trusty, old TI-84 Plus calculator. Beyond its strange format, there’s the
racy with minimal upward scaling. “It’s “Just how smart is it? Or isn’t it?” sheer amount of data—about one peta-
ahead of any of the trends that we were asks Cobbe. Though models like Min- byte per second, of which only a small
expecting,” says Karl Cobbe, a machine- erva might arrive at the same answer as high-quality amount is saved. To better
DECEMBER 2022  SPECTRUM.IEEE.ORG  7
NEWS
sift through the data, researchers want to train a sharp-eyed TELECOMMUNICATIONS
algorithm. To be effective, such an algorithm would need to
be incredibly speedy, executing in microseconds, Duarte says. How 5G’s Rollout Rattled
Machine-learning techniques like pruning and quantization,
Hundreds of Pilots Did
could allow algorithms to get there.
Machine learning is also allowing particle physicists to see C-band signals foul up
data in a different light. Instead of focusing on a single event—
say, a Higgs boson decaying to two photons—they are learning planes’ altimeters?
to think about the dozens of other events that happen during a
collision. Although there’s no causal relationship between any
two events, researchers are now embracing a more holistic view
BY MARK HARRIS
of the data, not just the piecemeal point of view that comes
from analyzing individual events.
More dramatically, machine learning has also forced physi- In January this year, at least three flights above the
cists to reconceive basic concepts. “I was imprecise in my own U.S. state of Tennessee simultaneously experi-
thinking about what a symmetry was,” says Jesse Thaler, a enced altimeter errors that made it “impossible
theoretical particle physicist at the Massachusetts Institute of to maintain assigned altitude,” according to one
Technology. “Forcing myself to teach a computer what a sym- of the pilots. One jet lost its autopilot completely, and
metry was, helped me understand what a symmetry actually reportedly had fire trucks waiting for it on landing.
is.” Symmetries require a reference frame—in other words, is In February, a passenger plane on approach to
the image of a distorted sphere in a mirror actually symmetri- the Louis Armstrong International Airport in New
cal? There’s no way of knowing without knowing if the mirror Orleans experienced erratic low-altitude warnings
itself is distorted. as it flew below 1,000 feet. “This sort of erroneous
These are still early days for machine learning in particle warning indications would be extremely distracting
physics, and researchers are effectively treating the technique in a more challenging environment such as low vis-
like a proverbial kitchen sink. “It may not be the right fit for ibility, icing conditions, etc,” the pilot wrote later.
every single problem in particle physics,” admits Duarte. In March, a commercial jet landing on autopilot
As some particle physicists delve into ML, an uncomfortable at Los Angeles International Airport suddenly went
question rears its head: Are they doing physics or computer into an aggressive descent just 100 feet above the
science? Stigma against coding—often not considered “real ground. “I took control of the aircraft and raised
physics”—already exists; similar concerns swirl around ML. the nose and landed,” its pilot reported. “It was a
Researchers who worry that ML will obscure the analysis of very alarming pushover by the autopilot. In [other]
very complicated collisions are building algorithms to provide conditions, it could have caused a crash.”
feedback in language humans can understand. But algorithms All three incidents—and many more this year—
may not be the only ones with responsibilities to communicate. were linked by pilots to problems with the aircrafts’
“On the one hand, we’d like to have a machine learn to think radio (radar) altimeters, which pilots rely on during
more like a physicist. We also just need to learn how to think a takeoff and landing, and to help avoid crashing into
little bit more like a machine,” Thaler says. “We need to learn mountains. These altimeters also feed into critical
to speak each other’s language.” autopilot, autothrottle, and instrument landing sys-
tems. According to an IEEE Spectrum analysis of
reports made to NASA’s Aviation Safety Reporting
System (ASRS), complaints of malfunctioning and
failing altimeters soared after the rollout earlier this
year of high-speed 5G wireless networks, which use
the same frequency band as the planes’ radar systems.
ASRS is a public database maintained by NASA
to encourage U.S.-based air and ground crew, and
air traffic controllers, to anonymously share safety
incidents and concerns. Between January and May,
there were 93 reports of faulty or failing radar altim-
eters, where a normal year might see only a handful.
January alone saw almost twice as many complaints
of malfunctioning altimeters as the previous five
years combined. In most, including the Tennessee
and Los Angeles incidents above, the reporter
referred to 5G interference.
Graph neural networks (GNNs) help particle physicists The U.S. Federal Communications Commission
recreate and reimagine the complex and manifold colli-
initially played down concerns that the new cell
sions at the Large Hadron Collider—pictured here in a RN
cross section of the heavy-ion ALICE detector. towers and devices might interfere with commercial CE
8  SPECTRUM.IEEE.ORG  DECEMBER 2022