Way, way back in the day, “computing” was the domain of analog circuits. No, they couldn’t add up columns of numbers, but they could solve complex differential and other equations, and did so fairly nicely in real time (within bandwidth limitations, of course) – once you set them up via manual gain controls and patch panels, like the computer in Figure 1.
Figure 1 This Donner 3500 was a small, desktop-sized analog computer; other all-analog computers occupied a full rack or even a small room. (Image source: Time-Line Computer Archive)
The core functional blocks for the analog functions (addition, subtraction, multiplication, division, differentiation, and integration) were based on the tube-based operational amplifier exemplified by the legendary K2-W from George A. Philbrick Researches, shown in Figure 2. These were soon replaced by discrete-transistor versions and eventually integrated circuits.
Figure 2 The GAP/R K2-W op amp, photo and schematic diagram; schematic values in meg-ohms and pF (courtesy of GAP/R alumnus Dan Sheingold, via http://protorit.blogspot.com)
Computers go beyond electronics. About 50 years ago, there was even excited talk of “fluidic” computers which used the Coanda effect (the tendency of a fluid jet to stay attached to a convex surface). They employed water or air in plastic channels to implement logic gates; the gates were interconnected by standard, flexible plastic tubing (References 1 and 2). The advantage was their noise immunity, but their size and physical awkwardness was among their many negatives. A typical four-input AND or OR gate alone was about half the size of a deck of cards. Air-based fluid computing is still a research topic using etched microchannels (not to be confused with the widely used fluidic microchannels of medical instrumentation), but let’s face it – it’s tough to compete with the realities of Moore’s “Law.”
The days of analog computing are pretty much gone, although there are holdouts and even some research is still being done (see Reference 3). Now, though, we associate “computing” with “digital.” (Of course, the electronic and physical reality is that digital circuits themselves are a niche subset of analog functions, but that’s another story.) Recently, too, there has been lots of high-end research and serious money going into quantum computing, although it’s not clear yet how much is hope, hype, or reality (see Reference 4). I’ll let the people who understand this better than I do, and the passage of time, make that determination.
There’s no need to restrict ourselves to analog, digital, or even quantum computing. Some researchers say that “biologic” computing is the really next big thing, if only we could figure it out. Sure, it may be slow (like the human brain), but it will also be a flexible, versatile, adaptable self-learning “machine” like nothing out there now or in the near future. After all, who really knows basics such as how (not where) the brain stores images and data, and how the brain is able to retrieve memories sometimes immediately and sometimes hours later when that thing you were trying to remember “pops” into your head. Or think of all the computing and electrical horsepower it requires to implement the autonomous vehicle, a task which just about anyone can learn to do using their a 3-pound (1.4 kg), <25 W, very slow biological computer called the human brain.
But why stop with those technologies? There’s research being done in optical computing, and given the bandwidth and speed of light, an optical computer could be fast and powerful. But controlling and switching the light’s passage is a real problem. Some designs use MEMS-based micromirrors (similar to the mechanism of Texas Instrument’s Digital Light Processing technology), but even that still has micro-sized moving parts with the associated speed and density issues.
Of course, researchers are investigating other ways to realize the potential of all-optical computing. A recent paper reported an intriguing approach developed by a collaboration between researchers at McMaster University (Canada) and the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS). They used a new type of hydrogel material tht offers reversible swelling and contracting. Low laser power causes the refractive index of this material to change and the hydrogel material also acts as a light pipe to contain the optical energy in a filament, similar to an optical fiber, Figure 3. The “switching” function is this: when the focused laser light hits an area of the gel, that area contracts slightly and changes its refractive index; the gel returns to its original state hen the light is turned off.
Figure 3 The lab-bench optical setup does not resemble a “computer” but initial steps of technology advances rarely resemble their practical result. (Image source: McMaster University)
There’s more to an optical computer than just changing refractive index and optical paths, although it does make a great photo, Figure 4. When multiple beams go through the hydrogel material, they interact and affect each other’s intensity, even at large distances or without their optical fields overlapping. “Though they are separated, the beams still see each other and change as a result,” said project leader Kalaichelvi Saravanamuttu, an associate professor at McMaster and paper co-author. By changing the refractive indices, the interaction between these multiple filaments of light can be stopped, started, managed, and read, producing a predictable output — an important first step in the switching and development of logic functions.
Figure 4 (A) The photoisomerization scheme of the hydrogel. (B) Photographs of chromophore-containing hydrogel monoliths employed used. (C) UV-visible absorbance spectra demonstrating reversible isomerization in solution. (D) Experimental setup (top) to probe laser self-trapping due to photoinduced local contraction of the hydrogel, schematically depicted on the bottom (see also Movie S1). A laser beam is focused onto the entrance face of the hydrogel while its exit face is imaged onto a CCD camera (see linked paper for full detailed caption). (Image source: McMaster University)
For more details, check out their seven-page paper with the challenging title “Opto-chemo-mechanical transduction in photoresponsive gels elicits switchable self-trapped beams with remote interactions” published in Proceedings of the National Academy of Science, along with a 47-page posted Supplementary Information packet and a 10-second video.
Can we think beyond semiconductor-based computing? Do you see any possibilities for non-electronic computing in the next few decades? If so, via what physical processes and phenomena?
- The Royal Society, 22 April 2019, “A brief history of liquid computers”
- Scientific American, Dec 1964 “Fluidics”
- IEEE Spectrum, 01 Dec 2017, “Not Your Father’s Analog Computer”
IEEE Spectrum, 24 Mar 2020, Here’s a Blueprint for a Practical Quantum Computer