Semiconductor Energy Laboratory: White Paper
September 2017
EXTREMELY LOW-POWER AI HARDWARE ENABLED BY CRYSTALLINE OXIDE SEMICONDUCTORS
Semiconductor Energy Laboratory (SEL): Extremely low-power AI chips can be
built with SEL's crystalline oxide semiconductor technology.
One factor enabling this is the extremely low off-state current of the FETs
utilizing crystalline oxide semiconductors, which we term OSFETs. The off-state
leakage current of the OSFET is extremely low. In fact, it is lower than that of
silicon FETs by 15 digits. This feature allows fabrication of devices with
extremely low power consumption, and also enables analog computation in
hardware implementations of artificial neural networks.
Thus, AI chips and systems that consume significantly less power can be
constructed.
EXTREMELY LOW-POWER AI HARDWARE ENABLED BY CRYSTALLINE OXIDE SEMICONDUCTORS
Semiconductor Energy Laboratory: White Paper Page 1
Introduction
SEL's crystalline oxide semiconductor (c-OS)
technology will reduce the enormous amount of
power needed for AI computations, which has been
an issue facing AI.
An FET utilizing a crystalline oxide semiconductor
material (OSFET) is characterized by its extremely low
off-state leakage current; when compared with Si
FETs, the OSFET has an off-state current that is 15
digits lower. Thus, it can be said that the OSFET is a
superb switch.
Using this superb switch, we have developed a
new kind of memory, the osMemory Logic (see FIG.
1). In this memory, the OSFET is connected to
another FET and a capacitor. A charge Q can be
stored in this cell when the OSFET is turned on (data
write). Subsequently, OSFET can be turned off to
eliminate the leakage current almost completely, so
that the stored charge Q can be retained for a long
time. The stored charge Q is input to the other FET's
gate, and the amount of current flowing across the
other FET changes according to the size of Q. This is
the principle of osMemory Logic's operation.
Conventional floating-gate non-volatile memory
writes data by injecting a charge into the gate
insulating film of a transistor. Because a large amount
of energy is necessary for charge injection, the gate
insulating film degrades relatively quickly, limiting
the maximum number of write cycles.
Conversely, the osMemory Logic can rewrite data
by simply turning the OSFET on and off. Thus, the
osMemory Logic can be considered an ideal memory
that does not degrade in principle.
An artificial neural network can perform analog
computations when the osMemory Logic is applied
to its hardware. This streamlines the computation
process, enabling an AI solution that has drastically
lower power consumption than conventional ones.
In this white paper, we will introduce the basics of
crystalline oxide semiconductor technology, focusing
on osMemory Logic, analog arithmetic circuits
constructed using the osMemory Logic, and artificial
neural networks.
Figure 1. Operating principles of osMemory Logic
EXTREMELY LOW-POWER AI HARDWARE ENABLED BY CRYSTALLINE OXIDE SEMICONDUCTORS
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1. Crystalline oxide semiconductor technology
The defining feature of the OSFET is its extremely
low off-state current, which is 70 yA (yoctoamperes,
yocto- is a prefix denoting 10-24) in 85C. This is 15
digits smaller than the off-state current of
conventional silicon transistors.
Such performance is realized by the wide band gap
common to the crystalline oxide semiconductor
(c-OS) material, and also as thermal excitation of the
electron-hole pair does not occur when the transistor
is off. Si has a band gap of 1.12 eV2). On the other
hand, the band gap of IGZO, a typical c-OS material,
is approximately 3.15 eV1).
In addition, we have found that the effective hole
mass of c-OS is heavier than that of Si (see Table. 1).
Therefore, the holes in c-OS do not contribute to
electric conduction and current induced by inversion
does not flow across the transistor. These traits of
c-OS materials contribute to the extremely low
off-state current of the OSFET (see FIG. 2).
Table 1. Effective mass of holes and electrons
IGZO1) Si2)
Hole effective mass mh*/me 11-40 0.49 (heavy)
0.16 (light)
Electron effective mass me*/me 0.23-0.25 0.98 (longitudinal)
0.19 (transverse)
(A) Id-Vg characteristics of Si and OSFET (B) Ioff of Si and OSFETs Figure 2. Off-state current (Ioff) comparison between Si and OSFETs
Currently, SEL is developing VLSI technology using
crystalline oxide semiconductor technology (termed
OSLSI) with UMC3), a partner with whom we have a
joint development agreement (JDA). We are
developing OSLSI for mass production, to introduce
extremely low power (XLP) devices to the market.
OSLSI can be fabricated using a 3D hybrid process in
which OSFETs are stacked on top of Si FETs fabricated
EXTREMELY LOW-POWER AI HARDWARE ENABLED BY CRYSTALLINE OXIDE SEMICONDUCTORS
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with existing technology platforms. When we
fabricate OSFETs with this process, we can build an IC
that consumes extremely low amount of power,
thanks to extremely low off-state current of the
OSFET.
We have prototyped a 60 nm node OSLSI chip that
can efficiently shut off the power supply utilizing the
features of the OSFET, achieving an exceptionally low
power consumption. Currently our chips' power
consumption figures are lower than those of
conventional chips by one order of magnitude. Our
target is to make the difference even larger, into
three orders of magnitude. Sample chips of this
technology are planned to be shipped out from the
end of 2017 to 2018.
OSLSI not only meets the demands of digital
operation in this IoT and big data age, but also the
needs of extremely low power consumption of
analog operation and analog/digital mixed signal
operation. It can be applied to a variety of devices
such as MCU, FPGA, embedded memory, etc.
One example of such an application is a
DRAM-type device called DOSRAM. Conventional
DRAM needs to be refreshed in regular periods on
the order of milliseconds. However, DOSRAM utilizes
the OSFET to make the refresh intervals longer so
that the device only needs to be refreshed once
every hour, or a few times in one year. Another
example is a normally-off CPU (see FIG. 3). The
normally-off CPU can shut down the power supply
when the CPU does not need to operate. Using these
techniques for low power consumption, we have
successfully built IC chips with remarkably low power
consumption.
Figure 3. Power consumption 4)
1) Murakami et al., Proc.AM-FPD’12 Dig., 171, 2012. 2) S. M. Sze and K. K. Ng, Physics of Semiconductor Devices, 3rd edn. New York: John Wiley, 2006. 3) UMC is a leading global semiconductor foundry headquartered in Hsinchu, Taiwan. Source: www.umc.com (UMC is currently world's No. 2 foundry.) 4) T. Onuki et al., Symp. VLSI Circuits, pp. 124–125, 2016.
EXTREMELY LOW-POWER AI HARDWARE ENABLED BY CRYSTALLINE OXIDE SEMICONDUCTORS
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Oxide semiconductor memory (osMemory Logic)
A memory device usually stores binary data, that is,
either 0 or 1. In contrast, the osMemory Logic uses
OSFET with its extremely low off-state current, and
thus is able to store levels more than just 0 or 1 in
one memory device..
The osMemory Logic can store 6-bit (64 levels)
data (See FIG. 4).
Another feature of multi-level osMemory Logic is
that it is a four-terminal device in which data write
and data read are performed with different terminals.
This distinguishes the multi-level osMemory Logic
from MRAM (Magnetoresistive Random Access
Memory) and FRAM (Ferroelectric Random Access
Memory) devices, which are two-terminal devices.
Four-terminal devices are more suited for storing
multiple levels of data, as data stored in two-terminal
devices (they have shared write/read terminals)
change their value during data read.
Figure 4. osMemory Logic and its performance
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2. AI (Artificial Intelligence) with c-OS technology
Using osMemory Logic, we can construct AI
solutions that consume little power.
Currently, artificial neural networks modeled after
the human brain are widely used in AI development
(see FIG. 5A). In an artificial neural network,
multiply-accumulate operation is performed using
weight coefficients (connection coefficients,
multipliers) and input data (multiplicands). In this
process, massively parallel computations are
required.
It is well known that GPU handles
multiply-accumulate operations of neural nets well.
However, if this kind of operation is to be performed
with digital circuits such as those in conventional
GPU, a circuit of an enormous scale will be necessary.
Furthermore, the results of the calculations will need
to be stored in a memory outside of the arithmetic
circuit, and accessing the memory will limit the
processing speed of this circuit.
Since long ago, there were expectations that
analog processing will result in a more efficient AI
solution. However, up until now, we do not have an
ideal memory that satisfies the demands in cell size
and memory precision.
As described above, the osMemory Logic
developed by SEL is characterized by its high
precision of 6 bits (64 levels) or higher. Thus, the
OSLSI with this memory can perform analog
multiply-accumulate operations in artificial neural
networks. When OSLSI is used in arithmetic
operations for artificial neural networks, there are
two advantages: the first is that the circuit can be
made much smaller than digital circuits, and the
second is that OSLSI can process massively parallel
arithmetic operations more easily than digital
circuits.
OSLSI takes a structure in which is formed by
stacking OSFET layers on top of Si LSI. This is termed
OS-Si hybrid structure, and this structure greatly
reduces energy losses during data transfers, as the
arithmetic circuit and the osMemory Logic can be
placed in locations that are extremely close to each
other (see FIG. 5B). This configuration is called
"on-site memory".
Utilizing these technologies, we can achieve low
power, smaller circuitry, and a power-efficient AI.
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Figure 5. Arithmetic processing in an artificial neural network and an analog arithmetic processing circuit
Multiply-accumulate circuits This section will describe multiply-accumulate
circuits constructed with osMemory Logic (see FIG. 6).
Using this multiply-accumulate circuit, we can have a
memory that has unlimited endurance (learning
becomes simple), consumes less power and area,
and performs arithmetic operations in a more
parallel manner.
The current that flows across the osMemory Logic
corresponds to the product of the weight coefficient
W and the input voltage X (W X). In addition, the
current I which is the sum of current from each
osMemory Logic cell corresponds to a sum of
products (I I0 I1 W0 X0 W1 X1). Furthermore,
the current Iout which is the result of subtracting
noise etc. (Inoise) from the current I corresponds to
the difference (Iout I Inoise). We can increase the
operation accuracy by using the difference (Iout) in
addition to the sum of products (I).
For example, the right formula can be expressed
with multiply-accumulate and difference operation
circuits in FIG. 6.
I I0 I1 W0 X0 W1 X1
Iout I Inoise
In addition, this multiply-accumulate circuit
employs the OS-Si hybrid structure, in which digital
and analog circuits can be formed in the same
process step. This makes the multiply-accumulators
efficient in terms of area and power, enabling an
embedded AI chip with various circuits and artificial
neural networks.
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Figure 6. Multiply-accumulate and difference operation circuits
Summary
This white paper described the basic information on
crystalline oxide semiconductor technology and AI
configurations that it enables. Another SEL white
paper, "AI Application Based on Crystalline Oxide
Semiconductor Technology", will describe the
performance of AI chips with the configurations
described here, and the application examples of
these chips. We hope you will take a look at them as
well.