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Katherine Derbyshire

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Katherine Derbyshire is a technical editor at Semiconductor Engineering.

Author's Latest Posts

What If We Had Bi-Directional RRAM?
By Katherine Derbyshire - 15 Feb, 2018 - Comments: 0

The ideal memristor device for neuromorphic computing would have linear and symmetric resistance behavior. Resistance would both increase and decrease gradually, allowing a direct correlation between the number of programming pulses and the resistance value. Real world RRAM devices, however, generally do not have these characteristics. In filamentary RRAM devices, the RESET operation can raise ... » read more

What’s Next In Neuromorphic Computing
By Katherine Derbyshire - 15 Feb, 2018 - Comments: 2

To integrate devices into functioning systems, it's necessary to consider what those systems are actually supposed to do. Regardless of the application, [getkc id="305" kc_name="machine learning"] tasks involve a training phase and an inference phase. In the training phase, the system is presented with a large dataset and learns how to "correctly" analyze it. In supervised learning, the data... » read more

How Good Is 95% Accuracy?
By Katherine Derbyshire - 28 Dec, 2017 - Comments: 0

Conventional, deterministic computers don’t make mistakes. They execute a predictable series of computations in response to any given input. The input might be mistaken. The logic behind the operations that are performed might be flawed. But the computer will always do exactly what it has been told to do. When unexpected results occur, they can be attributed to the programmer, the system manu... » read more

3D Neuromorphic Architectures
By Katherine Derbyshire - 11 Dec, 2017 - Comments: 1

Matrix multiplication is a critical operation in conventional neural networks. Each node of the network receives an input signal, multiplies it by some predetermined weight, and passes the result to the next layer of nodes. While the nature of the signal, the method used to determine the weights, and the desired result will all depend on the specific application, the computational task is simpl... » read more

Toward Neuromorphic Designs
By Katherine Derbyshire - 25 Oct, 2017 - Comments: 1

Part one of this series considered the mechanisms of learning and memory in biological brains. Each neuron has many fibers, which connect to adjacent neurons at synapses. The concentration of ions such as potassium and calcium inside the cell is different from the concentration outside. The cellular membrane thus serves as a capacitor. When a stimulus is received, the neuron releases neur... » read more

Terminology Beyond von Neumann
By Katherine Derbyshire - 21 Sep, 2017 - Comments: 0

Neural networks. Neuromorphic computing. Non-von Neumann architectures. As I’ve been researching my series on neuromorphic computing, I’ve encountered a lot of new terminology. It hasn’t always been easy to figure out exactly what’s being discussed. This explainer attempts to both clarify the terms used in my own articles and to help others sort through the rapidly growing literature in... » read more

Ruthenium Liners Give Way To Ruthenium Lines
By Katherine Derbyshire - 17 Aug, 2017 - Comments: 0

For several years now, integrated circuit manufacturers have been investigating alternative barrier layer materials for copper interconnects. As interconnect dimensions shrink, the barrier accounts for an increasing fraction of the total line volume. As previously reported, both cobalt and ruthenium have drawn substantial interest because they can serve as both barrier and seed layers, minimizi... » read more

Planes, Birdhouses And Image Recognition
By Katherine Derbyshire - 01 Aug, 2017 - Comments: 0

My recent blog post on the limits of neuromorphic computing took an optimistic view: even neuromorphic systems that are relatively crude by the standards of biological brains can still find commercially important applications. A few days after I finished it, I was reminded that the pessimists are not wrong when a friend of mine shared this image. Fig. 1: Trover Gourds in purple martin nest... » read more

Pessimism, Optimism And Neuromorphic Computing
By Katherine Derbyshire - 20 Jul, 2017 - Comments: 0

As I’ve been researching this series on neuromorphic computing, I’ve learned that there are two views of the field. One, which I’ll call the “optimist” view, often held by computer scientists and electrical engineers, focuses on the possibilities: self-driving cars. Homes that can learn their owners’ needs. Automated medical assistants. The other, the “pessimist” view, often hel... » read more

Neuromorphic Computing: Modeling The Brain
By Katherine Derbyshire - 15 Jun, 2017 - Comments: 8

Can you tell the difference between a pedestrian and a bicycle? How about between a skunk and a black and white cat? Or between your neighbor’s dog and a colt or fawn? Of course you can, and you probably can do that without much conscious thought. Humans are very good at interpreting the world around them, both visually and through other sensory input. Computers are not. Though their sheer... » read more

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